Data exploration and analysis with Python Pandas

20 Jan 2023 / 238 minutes to read

Elena Daehnhardt

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Table of Contents

Introduction

Data science is a multidisciplinary field involving scientific methods, procedures, algorithms, and techniques to extract knowledge and insights from structured and unstructured data. Data analysis uses statistical and computational approaches to identify data patterns, trends, and relationships. It plays a vital role in the data science process. It is typically used to prepare and preprocess the data, perform exploratory data analysis, build and evaluate models, extract insights and make data-driven decisions. In Data Science, we have so many terms explaining concepts and techniques that it is easy to need clarification and get a clear understanding of all data science components and steps.

In this post, I fill the gap by explaining data science’s two essential components: data analysis and exploration. To make things clear and precise, I will outline both approaches, compare them and show the usage of Python Pandas for data exploration and analysis. I will also show several practices using Pandas and graph drawing using Python. Please let me know should you have any questions or comments about this post.

Data Analysis vs. Data Exploration

What is Data Analysis?

Data analysis can help determine patterns, trends, and insights that may not be immediately evident from raw data. This can guide informed decision-making, improved processes and strategies, and the ability to measure the effectiveness of different approaches. Additionally, analyzing data can help see and diagnose issues and can be used to build predictive models that can inform future actions.

These are a few examples of how data analysis can improve business productivity.

Determining inefficiencies: Businesses can identify areas where operations are taking too long or resources are being wasted by analyzing data from different business processes. This can help them make changes that increase efficiency and lower expenses.
Targeted marketing: Data analysis can be used to better understand customer behavior and preferences. This helps businesses construct more targeted marketing campaigns that are more likely to be successful, which leads to increased sales and earnings.
Inventory management: By scrutinizing data on product sales and customer demand, businesses can optimize their stock levels, which can help them avoid stockouts and overstocking.
Quality control: Data analysis can identify production data patterns, which can help businesses find and fix problems before they result in defective products or customer complaints.
Predictive care: By analyzing data on equipment performance, companies can predict when maintenance will be needed and organize it proactively, which can prevent breakdowns and improve uptime.
Fraud detection: Data analysis can identify dishonest behavior patterns, which can help companies detect and prevent fraudulent transactions before they happen.

Data analysis is the process of using statistical and computational methods to extract meaningful insights from data.

The main steps in data analysis typically include the following:

Defining the problem and goals: This step involves defining the problem you want to solve and the specific questions or hypotheses you want to answer.
Data preparation: This step involves cleaning and preparing the data for analysis, including loading it into a suitable format, handling missing values, and transforming the data as needed.
Exploratory Data Analysis: This step involves exploring and summarizing the characteristics of the data, including understanding the structure and distribution of the data.
Modeling: This step involves building mathematical or statistical models to represent the data. The models can make predictions, classify data or identify patterns.
Evaluation: This step involves evaluating the performance of the models and comparing them with relevant benchmarks.
Communication of results: This step involves presenting and interpreting the analysis results clearly and meaningfully, creating a report or a presentation to share the findings.
Deployment: This step involves taking the results and putting them into action, using the models to make predictions or insights to inform business decisions.

These steps may only sometimes be strictly sequential, and there may be iterations and multiple rounds of analysis as needed to gain a thorough understanding of the data. The steps and techniques will vary depending on the type of data and the problem being addressed.

What is Data Exploration?

Data exploration analyzes and summarizes a dataset to understand its characteristics and properties. This may involve visualizing the data, identifying patterns and trends, and performing statistical analyses. The goal of data exploration is to gain insights into the data that can inform further analysis or modeling.

Data exploration typically includes the following steps:

Data loading and cleaning: This step involves loading the data into a suitable format, such as a Pandas DataFrame, and cleaning it to remove any errors, missing values, or irrelevant information.
Data understanding: This step involves understanding the structure of the data, including the number of rows and columns, the data types, and the range of values for each variable.
Univariate analysis: This step involves analyzing each variable individually to understand its distribution, central tendency, and variability. This can be done using simple statistics, such as mean, median, and standard deviation, and visualizations, such as histograms, box plots, and bar charts.
Multivariate analysis: This step involves analyzing the relationships between variables. This can be done using visualizations, such as scatter plots and heat maps, and correlation coefficients to measure the strength of the relationship between variables.
Data transformation: This step involves transforming the data to make it more suitable for analysis. This can include scaling the data, creating new variables, or encoding categorical variables.
Identifying outliers: This step involves identifying and analyzing any extreme values present in the data which can significantly impact the analysis.
Data summarisation: This step involves summarizing the main findings of the exploration and creating a report or a presentation to share the results.

These steps are not strictly sequential, and there may be iterations and multiple rounds of exploration to gain a thorough understanding of the data.

Their main differences

Data analysis and data exploration are related but distinct processes. The main differences between the two are:

Goal: Data analysis aims to extract meaningful insights, inform decisions, and support problem-solving. On the other hand, data exploration seeks to gain a general understanding of the data, identify patterns, and discover relationships.
Approach: Data analysis is typically more structured and formal, often guided by specific questions or hypotheses. On the other hand, data exploration is more open-ended, allowing you to explore the data without preconceived notions of what you might find.
Tools: Data analysis typically requires advanced statistical and computational tools to draw inferences from the data. On the other hand, data exploration can be done with various tools, including visualization and simple statistics.
Output: Data analysis produces quantifiable results, such as statistics and reports. On the other hand, data exploration often results in a deeper understanding of the data and the discovery of new questions to be further analyzed.
Audience: Data analysis is usually done for a specific audience, such as management or stakeholders. Data exploration is generally done by researchers or data scientists to gain insights and discover new questions.
Data exploration is often a preliminary step in the data analysis process, and it’s essential to do some level of exploration before diving into the analysis. Still, they are not mutually exclusive, and the two can be combined differently depending on the use case.

Can we accept that data exploration is a step in data analysis?

Yes, data exploration is often considered a step in the data analysis process. Data exploration is the process of gaining an initial understanding of the data, identifying patterns and relationships, and summarizing the main characteristics of the dataset. It is an essential step before starting any data analysis, as it allows you to identify potential issues with the data, such as outliers, missing values, and data errors, and to understand the distribution, central tendency, and variability of the variables.

Data exploration can also help identify the relationships between variables, which can inform the choice of models, techniques, and methods for the data analysis. Additionally, data exploration can assist in identifying new questions or hypotheses that can be further explored during the data analysis.

It is important to note that data exploration and analysis are not mutually exclusive and can be combined differently depending on the use case. The level and depth of data exploration can vary depending on the goals and complexity of the data analysis project.

Python’s Pandas library

Pandas is Python’s powerful and popular open-source data analysis and manipulation library, providing functions for working with time series data, filtering, grouping, transforming data, and handling missing values. Pandas supports reading and writing various file formats, such as CSV and SQL. Pandas is an integral part of many data analysis and machine learning pipelines. It is better fitting for working with data programmatically because of its integration with the rest of the Python ecosystem.

In Pandas documentation we read:

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

When reading this definition above, I wanted to know whether we can confidently define Pandas as a complete data analysis solution and why. Keep reading.

Installing and Importing Pandas

Installing Pandas

To install the Pandas package on Mac OS, you must have Python and pip (the Python package manager) installed on your system. To install the latest version of Pandas and all its dependencies, run this command in a terminal window:

pip install pandas

Suppose you still need to install Python and pip. In that case, you can download and install the latest version of Python from the official Python website (https://www.python.org/downloads/). This will install Python and pip on your system. Once Python and pip are installed, you can use pip to install the Pandas package by running the command above.

Alternatively, you can install Pandas using the Anaconda distribution, which comes with a pre-installed version of Pandas and many other popular data science libraries. To install Anaconda, visit the Anaconda website (https://www.anaconda.com/products/individual) and follow the instructions to download and install the latest version.

Importing Pandas

You must use the import statement to import the Pandas library into a Python script. This will import the Pandas library and give it the alias “pd”. Further, we will use the alias “pd” to access the functions and methods in the Pandas library.

import pandas as pd

Exploring the Titanic dataset

In the previous section, we have defined the main concepts of Data Science, Data Exploration, and Data Analysis. It is clear to me that Data Exploration is one of the steps in the stricter and former process of Data analysis, often requiring a definition of the hypothesis that we methodically explore using statistical and computational techniques. Most of the Pandas functionality we will see in this post mainly relates to data exploration. I will write up the differences and the exact Pandas features related to the data analysis.

Thus, in this post, we will use Pandas for “understanding” or exploring the Titanic dataset. The Titanic dataset is a well-known dataset that contains information about the passengers on the Titanic, a British passenger liner that sank in the North Atlantic Ocean in 1912 after colliding with an iceberg.

The Titanic dataset is a good dataset for learning data manipulation and analysis techniques, as it has a relatively small size and is easy to work with. The dataset includes information about each passenger, such as their name, age, gender, class (i.e., first, second, or third class), the fare paid, and whether they survived the disaster.

Loading Titanic Dataset

Pandas provides several methods to directly read data from various sources, for instance, from a webpage with the help of function pd.read_html(), or from a CSV file uploaded to a web server with the help of pd.read_csv() function using URL string pointing to a Comma-separated file (CSV).

url = 'https://raw.githubusercontent.com/edaehn/python_tutorials/main/titanic/train.csv'
titanic_df = pd.read_csv(url)

This will download the CSV file and load it into a Pandas DataFrame.

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.25	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.925	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.05	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7	G6	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.55	C103	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.05	NaN	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.275	NaN	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0	NaN	S
16	17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.125	NaN	Q
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0	NaN	S
18	19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)	female	31.0	1	0	345763	18.0	NaN	S
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.225	NaN	C
20	21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0	NaN	S
21	22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0	D56	S
22	23	1	3	McGowan, Miss. Anna “Annie”	female	15.0	0	0	330923	8.0292	NaN	Q
23	24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5	A6	S
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.075	NaN	S
25	26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)	female	38.0	1	5	347077	31.3875	NaN	S
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.225	NaN	C
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0	C23 C25 C27	S
28	29	1	3	O’Dwyer, Miss. Ellen “Nellie”	female	NaN	0	0	330959	7.8792	NaN	Q
29	30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
30	31	0	1	Uruchurtu, Don. Manuel E	male	40.0	0	0	PC 17601	27.7208	NaN	C
31	32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN	1	0	PC 17569	146.5208	B78	C
32	33	1	3	Glynn, Miss. Mary Agatha	female	NaN	0	0	335677	7.75	NaN	Q
33	34	0	2	Wheadon, Mr. Edward H	male	66.0	0	0	C.A. 24579	10.5	NaN	S
34	35	0	1	Meyer, Mr. Edgar Joseph	male	28.0	1	0	PC 17604	82.1708	NaN	C
35	36	0	1	Holverson, Mr. Alexander Oskar	male	42.0	1	0	113789	52.0	NaN	S
36	37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C
37	38	0	3	Cann, Mr. Ernest Charles	male	21.0	0	0	A./5. 2152	8.05	NaN	S
38	39	0	3	Vander Planke, Miss. Augusta Maria	female	18.0	2	0	345764	18.0	NaN	S
39	40	1	3	Nicola-Yarred, Miss. Jamila	female	14.0	1	0	2651	11.2417	NaN	C
40	41	0	3	Ahlin, Mrs. Johan (Johanna Persdotter Larsson)	female	40.0	1	0	7546	9.475	NaN	S
41	42	0	2	Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)	female	27.0	1	0	11668	21.0	NaN	S
42	43	0	3	Kraeff, Mr. Theodor	male	NaN	0	0	349253	7.8958	NaN	C
43	44	1	2	Laroche, Miss. Simonne Marie Anne Andree	female	3.0	1	2	SC/Paris 2123	41.5792	NaN	C
44	45	1	3	Devaney, Miss. Margaret Delia	female	19.0	0	0	330958	7.8792	NaN	Q
45	46	0	3	Rogers, Mr. William John	male	NaN	0	0	S.C./A.4. 23567	8.05	NaN	S
46	47	0	3	Lennon, Mr. Denis	male	NaN	1	0	370371	15.5	NaN	Q
47	48	1	3	O’Driscoll, Miss. Bridget	female	NaN	0	0	14311	7.75	NaN	Q
48	49	0	3	Samaan, Mr. Youssef	male	NaN	2	0	2662	21.6792	NaN	C
49	50	0	3	Arnold-Franchi, Mrs. Josef (Josefine Franchi)	female	18.0	1	0	349237	17.8	NaN	S
50	51	0	3	Panula, Master. Juha Niilo	male	7.0	4	1	3101295	39.6875	NaN	S
51	52	0	3	Nosworthy, Mr. Richard Cater	male	21.0	0	0	A/4. 39886	7.8	NaN	S
52	53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.0	1	0	PC 17572	76.7292	D33	C
53	54	1	2	Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)	female	29.0	1	0	2926	26.0	NaN	S
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
55	56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5	C52	S
56	57	1	2	Rugg, Miss. Emily	female	21.0	0	0	C.A. 31026	10.5	NaN	S
57	58	0	3	Novel, Mr. Mansouer	male	28.5	0	0	2697	7.2292	NaN	C
58	59	1	2	West, Miss. Constance Mirium	female	5.0	1	2	C.A. 34651	27.75	NaN	S
59	60	0	3	Goodwin, Master. William Frederick	male	11.0	5	2	CA 2144	46.9	NaN	S
60	61	0	3	Sirayanian, Mr. Orsen	male	22.0	0	0	2669	7.2292	NaN	C
61	62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
62	63	0	1	Harris, Mr. Henry Birkhardt	male	45.0	1	0	36973	83.475	C83	S
63	64	0	3	Skoog, Master. Harald	male	4.0	3	2	347088	27.9	NaN	S
64	65	0	1	Stewart, Mr. Albert A	male	NaN	0	0	PC 17605	27.7208	NaN	C
65	66	1	3	Moubarek, Master. Gerios	male	NaN	1	1	2661	15.2458	NaN	C
66	67	1	2	Nye, Mrs. (Elizabeth Ramell)	female	29.0	0	0	C.A. 29395	10.5	F33	S
67	68	0	3	Crease, Mr. Ernest James	male	19.0	0	0	S.P. 3464	8.1583	NaN	S
68	69	1	3	Andersson, Miss. Erna Alexandra	female	17.0	4	2	3101281	7.925	NaN	S
69	70	0	3	Kink, Mr. Vincenz	male	26.0	2	0	315151	8.6625	NaN	S
70	71	0	2	Jenkin, Mr. Stephen Curnow	male	32.0	0	0	C.A. 33111	10.5	NaN	S
71	72	0	3	Goodwin, Miss. Lillian Amy	female	16.0	5	2	CA 2144	46.9	NaN	S
72	73	0	2	Hood, Mr. Ambrose Jr	male	21.0	0	0	S.O.C. 14879	73.5	NaN	S
73	74	0	3	Chronopoulos, Mr. Apostolos	male	26.0	1	0	2680	14.4542	NaN	C
74	75	1	3	Bing, Mr. Lee	male	32.0	0	0	1601	56.4958	NaN	S
75	76	0	3	Moen, Mr. Sigurd Hansen	male	25.0	0	0	348123	7.65	F G73	S
76	77	0	3	Staneff, Mr. Ivan	male	NaN	0	0	349208	7.8958	NaN	S
77	78	0	3	Moutal, Mr. Rahamin Haim	male	NaN	0	0	374746	8.05	NaN	S
78	79	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	248738	29.0	NaN	S
79	80	1	3	Dowdell, Miss. Elizabeth	female	30.0	0	0	364516	12.475	NaN	S
80	81	0	3	Waelens, Mr. Achille	male	22.0	0	0	345767	9.0	NaN	S
81	82	1	3	Sheerlinck, Mr. Jan Baptist	male	29.0	0	0	345779	9.5	NaN	S
82	83	1	3	McDermott, Miss. Brigdet Delia	female	NaN	0	0	330932	7.7875	NaN	Q
83	84	0	1	Carrau, Mr. Francisco M	male	28.0	0	0	113059	47.1	NaN	S
84	85	1	2	Ilett, Miss. Bertha	female	17.0	0	0	SO/C 14885	10.5	NaN	S
85	86	1	3	Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)	female	33.0	3	0	3101278	15.85	NaN	S
86	87	0	3	Ford, Mr. William Neal	male	16.0	1	3	W./C. 6608	34.375	NaN	S
87	88	0	3	Slocovski, Mr. Selman Francis	male	NaN	0	0	SOTON/OQ 392086	8.05	NaN	S
88	89	1	1	Fortune, Miss. Mabel Helen	female	23.0	3	2	19950	263.0	C23 C25 C27	S
89	90	0	3	Celotti, Mr. Francesco	male	24.0	0	0	343275	8.05	NaN	S
90	91	0	3	Christmann, Mr. Emil	male	29.0	0	0	343276	8.05	NaN	S
91	92	0	3	Andreasson, Mr. Paul Edvin	male	20.0	0	0	347466	7.8542	NaN	S
92	93	0	1	Chaffee, Mr. Herbert Fuller	male	46.0	1	0	W.E.P. 5734	61.175	E31	S
93	94	0	3	Dean, Mr. Bertram Frank	male	26.0	1	2	C.A. 2315	20.575	NaN	S
94	95	0	3	Coxon, Mr. Daniel	male	59.0	0	0	364500	7.25	NaN	S
95	96	0	3	Shorney, Mr. Charles Joseph	male	NaN	0	0	374910	8.05	NaN	S
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
97	98	1	1	Greenfield, Mr. William Bertram	male	23.0	0	1	PC 17759	63.3583	D10 D12	C
98	99	1	2	Doling, Mrs. John T (Ada Julia Bone)	female	34.0	0	1	231919	23.0	NaN	S
99	100	0	2	Kantor, Mr. Sinai	male	34.0	1	0	244367	26.0	NaN	S
100	101	0	3	Petranec, Miss. Matilda	female	28.0	0	0	349245	7.8958	NaN	S
101	102	0	3	Petroff, Mr. Pastcho (“Pentcho”)	male	NaN	0	0	349215	7.8958	NaN	S
102	103	0	1	White, Mr. Richard Frasar	male	21.0	0	1	35281	77.2875	D26	S
103	104	0	3	Johansson, Mr. Gustaf Joel	male	33.0	0	0	7540	8.6542	NaN	S
104	105	0	3	Gustafsson, Mr. Anders Vilhelm	male	37.0	2	0	3101276	7.925	NaN	S
105	106	0	3	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	NaN	S
106	107	1	3	Salkjelsvik, Miss. Anna Kristine	female	21.0	0	0	343120	7.65	NaN	S
107	108	1	3	Moss, Mr. Albert Johan	male	NaN	0	0	312991	7.775	NaN	S
108	109	0	3	Rekic, Mr. Tido	male	38.0	0	0	349249	7.8958	NaN	S
109	110	1	3	Moran, Miss. Bertha	female	NaN	1	0	371110	24.15	NaN	Q
110	111	0	1	Porter, Mr. Walter Chamberlain	male	47.0	0	0	110465	52.0	C110	S
111	112	0	3	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	NaN	C
112	113	0	3	Barton, Mr. David John	male	22.0	0	0	324669	8.05	NaN	S
113	114	0	3	Jussila, Miss. Katriina	female	20.0	1	0	4136	9.825	NaN	S
114	115	0	3	Attalah, Miss. Malake	female	17.0	0	0	2627	14.4583	NaN	C
115	116	0	3	Pekoniemi, Mr. Edvard	male	21.0	0	0	STON/O 2. 3101294	7.925	NaN	S
116	117	0	3	Connors, Mr. Patrick	male	70.5	0	0	370369	7.75	NaN	Q
117	118	0	2	Turpin, Mr. William John Robert	male	29.0	1	0	11668	21.0	NaN	S
118	119	0	1	Baxter, Mr. Quigg Edmond	male	24.0	0	1	PC 17558	247.5208	B58 B60	C
119	120	0	3	Andersson, Miss. Ellis Anna Maria	female	2.0	4	2	347082	31.275	NaN	S
120	121	0	2	Hickman, Mr. Stanley George	male	21.0	2	0	S.O.C. 14879	73.5	NaN	S
121	122	0	3	Moore, Mr. Leonard Charles	male	NaN	0	0	A4. 54510	8.05	NaN	S
122	123	0	2	Nasser, Mr. Nicholas	male	32.5	1	0	237736	30.0708	NaN	C
123	124	1	2	Webber, Miss. Susan	female	32.5	0	0	27267	13.0	E101	S
124	125	0	1	White, Mr. Percival Wayland	male	54.0	0	1	35281	77.2875	D26	S
125	126	1	3	Nicola-Yarred, Master. Elias	male	12.0	1	0	2651	11.2417	NaN	C
126	127	0	3	McMahon, Mr. Martin	male	NaN	0	0	370372	7.75	NaN	Q
127	128	1	3	Madsen, Mr. Fridtjof Arne	male	24.0	0	0	C 17369	7.1417	NaN	S
128	129	1	3	Peter, Miss. Anna	female	NaN	1	1	2668	22.3583	F E69	C
129	130	0	3	Ekstrom, Mr. Johan	male	45.0	0	0	347061	6.975	NaN	S
130	131	0	3	Drazenoic, Mr. Jozef	male	33.0	0	0	349241	7.8958	NaN	C
131	132	0	3	Coelho, Mr. Domingos Fernandeo	male	20.0	0	0	SOTON/O.Q. 3101307	7.05	NaN	S
132	133	0	3	Robins, Mrs. Alexander A (Grace Charity Laury)	female	47.0	1	0	A/5. 3337	14.5	NaN	S
133	134	1	2	Weisz, Mrs. Leopold (Mathilde Francoise Pede)	female	29.0	1	0	228414	26.0	NaN	S
134	135	0	2	Sobey, Mr. Samuel James Hayden	male	25.0	0	0	C.A. 29178	13.0	NaN	S
135	136	0	2	Richard, Mr. Emile	male	23.0	0	0	SC/PARIS 2133	15.0458	NaN	C
136	137	1	1	Newsom, Miss. Helen Monypeny	female	19.0	0	2	11752	26.2833	D47	S
137	138	0	1	Futrelle, Mr. Jacques Heath	male	37.0	1	0	113803	53.1	C123	S
138	139	0	3	Osen, Mr. Olaf Elon	male	16.0	0	0	7534	9.2167	NaN	S
139	140	0	1	Giglio, Mr. Victor	male	24.0	0	0	PC 17593	79.2	B86	C
140	141	0	3	Boulos, Mrs. Joseph (Sultana)	female	NaN	0	2	2678	15.2458	NaN	C
141	142	1	3	Nysten, Miss. Anna Sofia	female	22.0	0	0	347081	7.75	NaN	S
142	143	1	3	Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)	female	24.0	1	0	STON/O2. 3101279	15.85	NaN	S
143	144	0	3	Burke, Mr. Jeremiah	male	19.0	0	0	365222	6.75	NaN	Q
144	145	0	2	Andrew, Mr. Edgardo Samuel	male	18.0	0	0	231945	11.5	NaN	S
145	146	0	2	Nicholls, Mr. Joseph Charles	male	19.0	1	1	C.A. 33112	36.75	NaN	S
146	147	1	3	Andersson, Mr. August Edvard (“Wennerstrom”)	male	27.0	0	0	350043	7.7958	NaN	S
147	148	0	3	Ford, Miss. Robina Maggie “Ruby”	female	9.0	2	2	W./C. 6608	34.375	NaN	S
148	149	0	2	Navratil, Mr. Michel (“Louis M Hoffman”)	male	36.5	0	2	230080	26.0	F2	S
149	150	0	2	Byles, Rev. Thomas Roussel Davids	male	42.0	0	0	244310	13.0	NaN	S
150	151	0	2	Bateman, Rev. Robert James	male	51.0	0	0	S.O.P. 1166	12.525	NaN	S
151	152	1	1	Pears, Mrs. Thomas (Edith Wearne)	female	22.0	1	0	113776	66.6	C2	S
152	153	0	3	Meo, Mr. Alfonzo	male	55.5	0	0	A.5. 11206	8.05	NaN	S
153	154	0	3	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	14.5	NaN	S
154	155	0	3	Olsen, Mr. Ole Martin	male	NaN	0	0	Fa 265302	7.3125	NaN	S
155	156	0	1	Williams, Mr. Charles Duane	male	51.0	0	1	PC 17597	61.3792	NaN	C
156	157	1	3	Gilnagh, Miss. Katherine “Katie”	female	16.0	0	0	35851	7.7333	NaN	Q
157	158	0	3	Corn, Mr. Harry	male	30.0	0	0	SOTON/OQ 392090	8.05	NaN	S
158	159	0	3	Smiljanic, Mr. Mile	male	NaN	0	0	315037	8.6625	NaN	S
159	160	0	3	Sage, Master. Thomas Henry	male	NaN	8	2	CA. 2343	69.55	NaN	S
160	161	0	3	Cribb, Mr. John Hatfield	male	44.0	0	1	371362	16.1	NaN	S
161	162	1	2	Watt, Mrs. James (Elizabeth “Bessie” Inglis Milne)	female	40.0	0	0	C.A. 33595	15.75	NaN	S
162	163	0	3	Bengtsson, Mr. John Viktor	male	26.0	0	0	347068	7.775	NaN	S
163	164	0	3	Calic, Mr. Jovo	male	17.0	0	0	315093	8.6625	NaN	S
164	165	0	3	Panula, Master. Eino Viljami	male	1.0	4	1	3101295	39.6875	NaN	S
165	166	1	3	Goldsmith, Master. Frank John William “Frankie”	male	9.0	0	2	363291	20.525	NaN	S
166	167	1	1	Chibnall, Mrs. (Edith Martha Bowerman)	female	NaN	0	1	113505	55.0	E33	S
167	168	0	3	Skoog, Mrs. William (Anna Bernhardina Karlsson)	female	45.0	1	4	347088	27.9	NaN	S
168	169	0	1	Baumann, Mr. John D	male	NaN	0	0	PC 17318	25.925	NaN	S
169	170	0	3	Ling, Mr. Lee	male	28.0	0	0	1601	56.4958	NaN	S
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0	0	0	111240	33.5	B19	S
171	172	0	3	Rice, Master. Arthur	male	4.0	4	1	382652	29.125	NaN	Q
172	173	1	3	Johnson, Miss. Eleanor Ileen	female	1.0	1	1	347742	11.1333	NaN	S
173	174	0	3	Sivola, Mr. Antti Wilhelm	male	21.0	0	0	STON/O 2. 3101280	7.925	NaN	S
174	175	0	1	Smith, Mr. James Clinch	male	56.0	0	0	17764	30.6958	A7	C
175	176	0	3	Klasen, Mr. Klas Albin	male	18.0	1	1	350404	7.8542	NaN	S
176	177	0	3	Lefebre, Master. Henry Forbes	male	NaN	3	1	4133	25.4667	NaN	S
177	178	0	1	Isham, Miss. Ann Elizabeth	female	50.0	0	0	PC 17595	28.7125	C49	C
178	179	0	2	Hale, Mr. Reginald	male	30.0	0	0	250653	13.0	NaN	S
179	180	0	3	Leonard, Mr. Lionel	male	36.0	0	0	LINE	0.0	NaN	S
180	181	0	3	Sage, Miss. Constance Gladys	female	NaN	8	2	CA. 2343	69.55	NaN	S
181	182	0	2	Pernot, Mr. Rene	male	NaN	0	0	SC/PARIS 2131	15.05	NaN	C
182	183	0	3	Asplund, Master. Clarence Gustaf Hugo	male	9.0	4	2	347077	31.3875	NaN	S
183	184	1	2	Becker, Master. Richard F	male	1.0	2	1	230136	39.0	F4	S
184	185	1	3	Kink-Heilmann, Miss. Luise Gretchen	female	4.0	0	2	315153	22.025	NaN	S
185	186	0	1	Rood, Mr. Hugh Roscoe	male	NaN	0	0	113767	50.0	A32	S
186	187	1	3	O’Brien, Mrs. Thomas (Johanna “Hannah” Godfrey)	female	NaN	1	0	370365	15.5	NaN	Q
187	188	1	1	Romaine, Mr. Charles Hallace (“Mr C Rolmane”)	male	45.0	0	0	111428	26.55	NaN	S
188	189	0	3	Bourke, Mr. John	male	40.0	1	1	364849	15.5	NaN	Q
189	190	0	3	Turcin, Mr. Stjepan	male	36.0	0	0	349247	7.8958	NaN	S
190	191	1	2	Pinsky, Mrs. (Rosa)	female	32.0	0	0	234604	13.0	NaN	S
191	192	0	2	Carbines, Mr. William	male	19.0	0	0	28424	13.0	NaN	S
192	193	1	3	Andersen-Jensen, Miss. Carla Christine Nielsine	female	19.0	1	0	350046	7.8542	NaN	S
193	194	1	2	Navratil, Master. Michel M	male	3.0	1	1	230080	26.0	F2	S
194	195	1	1	Brown, Mrs. James Joseph (Margaret Tobin)	female	44.0	0	0	PC 17610	27.7208	B4	C
195	196	1	1	Lurette, Miss. Elise	female	58.0	0	0	PC 17569	146.5208	B80	C
196	197	0	3	Mernagh, Mr. Robert	male	NaN	0	0	368703	7.75	NaN	Q
197	198	0	3	Olsen, Mr. Karl Siegwart Andreas	male	42.0	0	1	4579	8.4042	NaN	S
198	199	1	3	Madigan, Miss. Margaret “Maggie”	female	NaN	0	0	370370	7.75	NaN	Q
199	200	0	2	Yrois, Miss. Henriette (“Mrs Harbeck”)	female	24.0	0	0	248747	13.0	NaN	S
200	201	0	3	Vande Walle, Mr. Nestor Cyriel	male	28.0	0	0	345770	9.5	NaN	S
201	202	0	3	Sage, Mr. Frederick	male	NaN	8	2	CA. 2343	69.55	NaN	S
202	203	0	3	Johanson, Mr. Jakob Alfred	male	34.0	0	0	3101264	6.4958	NaN	S
203	204	0	3	Youseff, Mr. Gerious	male	45.5	0	0	2628	7.225	NaN	C
204	205	1	3	Cohen, Mr. Gurshon “Gus”	male	18.0	0	0	A/5 3540	8.05	NaN	S
205	206	0	3	Strom, Miss. Telma Matilda	female	2.0	0	1	347054	10.4625	G6	S
206	207	0	3	Backstrom, Mr. Karl Alfred	male	32.0	1	0	3101278	15.85	NaN	S
207	208	1	3	Albimona, Mr. Nassef Cassem	male	26.0	0	0	2699	18.7875	NaN	C
208	209	1	3	Carr, Miss. Helen “Ellen”	female	16.0	0	0	367231	7.75	NaN	Q
209	210	1	1	Blank, Mr. Henry	male	40.0	0	0	112277	31.0	A31	C
210	211	0	3	Ali, Mr. Ahmed	male	24.0	0	0	SOTON/O.Q. 3101311	7.05	NaN	S
211	212	1	2	Cameron, Miss. Clear Annie	female	35.0	0	0	F.C.C. 13528	21.0	NaN	S
212	213	0	3	Perkin, Mr. John Henry	male	22.0	0	0	A/5 21174	7.25	NaN	S
213	214	0	2	Givard, Mr. Hans Kristensen	male	30.0	0	0	250646	13.0	NaN	S
214	215	0	3	Kiernan, Mr. Philip	male	NaN	1	0	367229	7.75	NaN	Q
215	216	1	1	Newell, Miss. Madeleine	female	31.0	1	0	35273	113.275	D36	C
216	217	1	3	Honkanen, Miss. Eliina	female	27.0	0	0	STON/O2. 3101283	7.925	NaN	S
217	218	0	2	Jacobsohn, Mr. Sidney Samuel	male	42.0	1	0	243847	27.0	NaN	S
218	219	1	1	Bazzani, Miss. Albina	female	32.0	0	0	11813	76.2917	D15	C
219	220	0	2	Harris, Mr. Walter	male	30.0	0	0	W/C 14208	10.5	NaN	S
220	221	1	3	Sunderland, Mr. Victor Francis	male	16.0	0	0	SOTON/OQ 392089	8.05	NaN	S
221	222	0	2	Bracken, Mr. James H	male	27.0	0	0	220367	13.0	NaN	S
222	223	0	3	Green, Mr. George Henry	male	51.0	0	0	21440	8.05	NaN	S
223	224	0	3	Nenkoff, Mr. Christo	male	NaN	0	0	349234	7.8958	NaN	S
224	225	1	1	Hoyt, Mr. Frederick Maxfield	male	38.0	1	0	19943	90.0	C93	S
225	226	0	3	Berglund, Mr. Karl Ivar Sven	male	22.0	0	0	PP 4348	9.35	NaN	S
226	227	1	2	Mellors, Mr. William John	male	19.0	0	0	SW/PP 751	10.5	NaN	S
227	228	0	3	Lovell, Mr. John Hall (“Henry”)	male	20.5	0	0	A/5 21173	7.25	NaN	S
228	229	0	2	Fahlstrom, Mr. Arne Jonas	male	18.0	0	0	236171	13.0	NaN	S
229	230	0	3	Lefebre, Miss. Mathilde	female	NaN	3	1	4133	25.4667	NaN	S
230	231	1	1	Harris, Mrs. Henry Birkhardt (Irene Wallach)	female	35.0	1	0	36973	83.475	C83	S
231	232	0	3	Larsson, Mr. Bengt Edvin	male	29.0	0	0	347067	7.775	NaN	S
232	233	0	2	Sjostedt, Mr. Ernst Adolf	male	59.0	0	0	237442	13.5	NaN	S
233	234	1	3	Asplund, Miss. Lillian Gertrud	female	5.0	4	2	347077	31.3875	NaN	S
234	235	0	2	Leyson, Mr. Robert William Norman	male	24.0	0	0	C.A. 29566	10.5	NaN	S
235	236	0	3	Harknett, Miss. Alice Phoebe	female	NaN	0	0	W./C. 6609	7.55	NaN	S
236	237	0	2	Hold, Mr. Stephen	male	44.0	1	0	26707	26.0	NaN	S
237	238	1	2	Collyer, Miss. Marjorie “Lottie”	female	8.0	0	2	C.A. 31921	26.25	NaN	S
238	239	0	2	Pengelly, Mr. Frederick William	male	19.0	0	0	28665	10.5	NaN	S
239	240	0	2	Hunt, Mr. George Henry	male	33.0	0	0	SCO/W 1585	12.275	NaN	S
240	241	0	3	Zabour, Miss. Thamine	female	NaN	1	0	2665	14.4542	NaN	C
241	242	1	3	Murphy, Miss. Katherine “Kate”	female	NaN	1	0	367230	15.5	NaN	Q
242	243	0	2	Coleridge, Mr. Reginald Charles	male	29.0	0	0	W./C. 14263	10.5	NaN	S
243	244	0	3	Maenpaa, Mr. Matti Alexanteri	male	22.0	0	0	STON/O 2. 3101275	7.125	NaN	S
244	245	0	3	Attalah, Mr. Sleiman	male	30.0	0	0	2694	7.225	NaN	C
245	246	0	1	Minahan, Dr. William Edward	male	44.0	2	0	19928	90.0	C78	Q
246	247	0	3	Lindahl, Miss. Agda Thorilda Viktoria	female	25.0	0	0	347071	7.775	NaN	S
247	248	1	2	Hamalainen, Mrs. William (Anna)	female	24.0	0	2	250649	14.5	NaN	S
248	249	1	1	Beckwith, Mr. Richard Leonard	male	37.0	1	1	11751	52.5542	D35	S
249	250	0	2	Carter, Rev. Ernest Courtenay	male	54.0	1	0	244252	26.0	NaN	S
250	251	0	3	Reed, Mr. James George	male	NaN	0	0	362316	7.25	NaN	S
251	252	0	3	Strom, Mrs. Wilhelm (Elna Matilda Persson)	female	29.0	1	1	347054	10.4625	G6	S
252	253	0	1	Stead, Mr. William Thomas	male	62.0	0	0	113514	26.55	C87	S
253	254	0	3	Lobb, Mr. William Arthur	male	30.0	1	0	A/5. 3336	16.1	NaN	S
254	255	0	3	Rosblom, Mrs. Viktor (Helena Wilhelmina)	female	41.0	0	2	370129	20.2125	NaN	S
255	256	1	3	Touma, Mrs. Darwis (Hanne Youssef Razi)	female	29.0	0	2	2650	15.2458	NaN	C
256	257	1	1	Thorne, Mrs. Gertrude Maybelle	female	NaN	0	0	PC 17585	79.2	NaN	C
257	258	1	1	Cherry, Miss. Gladys	female	30.0	0	0	110152	86.5	B77	S
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
259	260	1	2	Parrish, Mrs. (Lutie Davis)	female	50.0	0	1	230433	26.0	NaN	S
260	261	0	3	Smith, Mr. Thomas	male	NaN	0	0	384461	7.75	NaN	Q
261	262	1	3	Asplund, Master. Edvin Rojj Felix	male	3.0	4	2	347077	31.3875	NaN	S
262	263	0	1	Taussig, Mr. Emil	male	52.0	1	1	110413	79.65	E67	S
263	264	0	1	Harrison, Mr. William	male	40.0	0	0	112059	0.0	B94	S
264	265	0	3	Henry, Miss. Delia	female	NaN	0	0	382649	7.75	NaN	Q
265	266	0	2	Reeves, Mr. David	male	36.0	0	0	C.A. 17248	10.5	NaN	S
266	267	0	3	Panula, Mr. Ernesti Arvid	male	16.0	4	1	3101295	39.6875	NaN	S
267	268	1	3	Persson, Mr. Ernst Ulrik	male	25.0	1	0	347083	7.775	NaN	S
268	269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.0	0	1	PC 17582	153.4625	C125	S
269	270	1	1	Bissette, Miss. Amelia	female	35.0	0	0	PC 17760	135.6333	C99	S
270	271	0	1	Cairns, Mr. Alexander	male	NaN	0	0	113798	31.0	NaN	S
271	272	1	3	Tornquist, Mr. William Henry	male	25.0	0	0	LINE	0.0	NaN	S
272	273	1	2	Mellinger, Mrs. (Elizabeth Anne Maidment)	female	41.0	0	1	250644	19.5	NaN	S
273	274	0	1	Natsch, Mr. Charles H	male	37.0	0	1	PC 17596	29.7	C118	C
274	275	1	3	Healy, Miss. Hanora “Nora”	female	NaN	0	0	370375	7.75	NaN	Q
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
276	277	0	3	Lindblom, Miss. Augusta Charlotta	female	45.0	0	0	347073	7.75	NaN	S
277	278	0	2	Parkes, Mr. Francis “Frank”	male	NaN	0	0	239853	0.0	NaN	S
278	279	0	3	Rice, Master. Eric	male	7.0	4	1	382652	29.125	NaN	Q
279	280	1	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	1	C.A. 2673	20.25	NaN	S
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.75	NaN	Q
281	282	0	3	Olsson, Mr. Nils Johan Goransson	male	28.0	0	0	347464	7.8542	NaN	S
282	283	0	3	de Pelsmaeker, Mr. Alfons	male	16.0	0	0	345778	9.5	NaN	S
283	284	1	3	Dorking, Mr. Edward Arthur	male	19.0	0	0	A/5. 10482	8.05	NaN	S
284	285	0	1	Smith, Mr. Richard William	male	NaN	0	0	113056	26.0	A19	S
285	286	0	3	Stankovic, Mr. Ivan	male	33.0	0	0	349239	8.6625	NaN	C
286	287	1	3	de Mulder, Mr. Theodore	male	30.0	0	0	345774	9.5	NaN	S
287	288	0	3	Naidenoff, Mr. Penko	male	22.0	0	0	349206	7.8958	NaN	S
288	289	1	2	Hosono, Mr. Masabumi	male	42.0	0	0	237798	13.0	NaN	S
289	290	1	3	Connolly, Miss. Kate	female	22.0	0	0	370373	7.75	NaN	Q
290	291	1	1	Barber, Miss. Ellen “Nellie”	female	26.0	0	0	19877	78.85	NaN	S
291	292	1	1	Bishop, Mrs. Dickinson H (Helen Walton)	female	19.0	1	0	11967	91.0792	B49	C
292	293	0	2	Levy, Mr. Rene Jacques	male	36.0	0	0	SC/Paris 2163	12.875	D	C
293	294	0	3	Haas, Miss. Aloisia	female	24.0	0	0	349236	8.85	NaN	S
294	295	0	3	Mineff, Mr. Ivan	male	24.0	0	0	349233	7.8958	NaN	S
295	296	0	1	Lewy, Mr. Ervin G	male	NaN	0	0	PC 17612	27.7208	NaN	C
296	297	0	3	Hanna, Mr. Mansour	male	23.5	0	0	2693	7.2292	NaN	C
297	298	0	1	Allison, Miss. Helen Loraine	female	2.0	1	2	113781	151.55	C22 C26	S
298	299	1	1	Saalfeld, Mr. Adolphe	male	NaN	0	0	19988	30.5	C106	S
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
300	301	1	3	Kelly, Miss. Anna Katherine “Annie Kate”	female	NaN	0	0	9234	7.75	NaN	Q
301	302	1	3	McCoy, Mr. Bernard	male	NaN	2	0	367226	23.25	NaN	Q
302	303	0	3	Johnson, Mr. William Cahoone Jr	male	19.0	0	0	LINE	0.0	NaN	S
303	304	1	2	Keane, Miss. Nora A	female	NaN	0	0	226593	12.35	E101	Q
304	305	0	3	Williams, Mr. Howard Hugh “Harry”	male	NaN	0	0	A/5 2466	8.05	NaN	S
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.55	C22 C26	S
306	307	1	1	Fleming, Miss. Margaret	female	NaN	0	0	17421	110.8833	NaN	C
307	308	1	1	Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)	female	17.0	1	0	PC 17758	108.9	C65	C
308	309	0	2	Abelson, Mr. Samuel	male	30.0	1	0	P/PP 3381	24.0	NaN	C
309	310	1	1	Francatelli, Miss. Laura Mabel	female	30.0	0	0	PC 17485	56.9292	E36	C
310	311	1	1	Hays, Miss. Margaret Bechstein	female	24.0	0	0	11767	83.1583	C54	C
311	312	1	1	Ryerson, Miss. Emily Borie	female	18.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C
312	313	0	2	Lahtinen, Mrs. William (Anna Sylfven)	female	26.0	1	1	250651	26.0	NaN	S
313	314	0	3	Hendekovic, Mr. Ignjac	male	28.0	0	0	349243	7.8958	NaN	S
314	315	0	2	Hart, Mr. Benjamin	male	43.0	1	1	F.C.C. 13529	26.25	NaN	S
315	316	1	3	Nilsson, Miss. Helmina Josefina	female	26.0	0	0	347470	7.8542	NaN	S
316	317	1	2	Kantor, Mrs. Sinai (Miriam Sternin)	female	24.0	1	0	244367	26.0	NaN	S
317	318	0	2	Moraweck, Dr. Ernest	male	54.0	0	0	29011	14.0	NaN	S
318	319	1	1	Wick, Miss. Mary Natalie	female	31.0	0	2	36928	164.8667	C7	S
319	320	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)	female	40.0	1	1	16966	134.5	E34	C
320	321	0	3	Dennis, Mr. Samuel	male	22.0	0	0	A/5 21172	7.25	NaN	S
321	322	0	3	Danoff, Mr. Yoto	male	27.0	0	0	349219	7.8958	NaN	S
322	323	1	2	Slayter, Miss. Hilda Mary	female	30.0	0	0	234818	12.35	NaN	Q
323	324	1	2	Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)	female	22.0	1	1	248738	29.0	NaN	S
324	325	0	3	Sage, Mr. George John Jr	male	NaN	8	2	CA. 2343	69.55	NaN	S
325	326	1	1	Young, Miss. Marie Grice	female	36.0	0	0	PC 17760	135.6333	C32	C
326	327	0	3	Nysveen, Mr. Johan Hansen	male	61.0	0	0	345364	6.2375	NaN	S
327	328	1	2	Ball, Mrs. (Ada E Hall)	female	36.0	0	0	28551	13.0	D	S
328	329	1	3	Goldsmith, Mrs. Frank John (Emily Alice Brown)	female	31.0	1	1	363291	20.525	NaN	S
329	330	1	1	Hippach, Miss. Jean Gertrude	female	16.0	0	1	111361	57.9792	B18	C
330	331	1	3	McCoy, Miss. Agnes	female	NaN	2	0	367226	23.25	NaN	Q
331	332	0	1	Partner, Mr. Austen	male	45.5	0	0	113043	28.5	C124	S
332	333	0	1	Graham, Mr. George Edward	male	38.0	0	1	PC 17582	153.4625	C91	S
333	334	0	3	Vander Planke, Mr. Leo Edmondus	male	16.0	2	0	345764	18.0	NaN	S
334	335	1	1	Frauenthal, Mrs. Henry William (Clara Heinsheimer)	female	NaN	1	0	PC 17611	133.65	NaN	S
335	336	0	3	Denkoff, Mr. Mitto	male	NaN	0	0	349225	7.8958	NaN	S
336	337	0	1	Pears, Mr. Thomas Clinton	male	29.0	1	0	113776	66.6	C2	S
337	338	1	1	Burns, Miss. Elizabeth Margaret	female	41.0	0	0	16966	134.5	E40	C
338	339	1	3	Dahl, Mr. Karl Edwart	male	45.0	0	0	7598	8.05	NaN	S
339	340	0	1	Blackwell, Mr. Stephen Weart	male	45.0	0	0	113784	35.5	T	S
340	341	1	2	Navratil, Master. Edmond Roger	male	2.0	1	1	230080	26.0	F2	S
341	342	1	1	Fortune, Miss. Alice Elizabeth	female	24.0	3	2	19950	263.0	C23 C25 C27	S
342	343	0	2	Collander, Mr. Erik Gustaf	male	28.0	0	0	248740	13.0	NaN	S
343	344	0	2	Sedgwick, Mr. Charles Frederick Waddington	male	25.0	0	0	244361	13.0	NaN	S
344	345	0	2	Fox, Mr. Stanley Hubert	male	36.0	0	0	229236	13.0	NaN	S
345	346	1	2	Brown, Miss. Amelia “Mildred”	female	24.0	0	0	248733	13.0	F33	S
346	347	1	2	Smith, Miss. Marion Elsie	female	40.0	0	0	31418	13.0	NaN	S
347	348	1	3	Davison, Mrs. Thomas Henry (Mary E Finck)	female	NaN	1	0	386525	16.1	NaN	S
348	349	1	3	Coutts, Master. William Loch “William”	male	3.0	1	1	C.A. 37671	15.9	NaN	S
349	350	0	3	Dimic, Mr. Jovan	male	42.0	0	0	315088	8.6625	NaN	S
350	351	0	3	Odahl, Mr. Nils Martin	male	23.0	0	0	7267	9.225	NaN	S
351	352	0	1	Williams-Lambert, Mr. Fletcher Fellows	male	NaN	0	0	113510	35.0	C128	S
352	353	0	3	Elias, Mr. Tannous	male	15.0	1	1	2695	7.2292	NaN	C
353	354	0	3	Arnold-Franchi, Mr. Josef	male	25.0	1	0	349237	17.8	NaN	S
354	355	0	3	Yousif, Mr. Wazli	male	NaN	0	0	2647	7.225	NaN	C
355	356	0	3	Vanden Steen, Mr. Leo Peter	male	28.0	0	0	345783	9.5	NaN	S
356	357	1	1	Bowerman, Miss. Elsie Edith	female	22.0	0	1	113505	55.0	E33	S
357	358	0	2	Funk, Miss. Annie Clemmer	female	38.0	0	0	237671	13.0	NaN	S
358	359	1	3	McGovern, Miss. Mary	female	NaN	0	0	330931	7.8792	NaN	Q
359	360	1	3	Mockler, Miss. Helen Mary “Ellie”	female	NaN	0	0	330980	7.8792	NaN	Q
360	361	0	3	Skoog, Mr. Wilhelm	male	40.0	1	4	347088	27.9	NaN	S
361	362	0	2	del Carlo, Mr. Sebastiano	male	29.0	1	0	SC/PARIS 2167	27.7208	NaN	C
362	363	0	3	Barbara, Mrs. (Catherine David)	female	45.0	0	1	2691	14.4542	NaN	C
363	364	0	3	Asim, Mr. Adola	male	35.0	0	0	SOTON/O.Q. 3101310	7.05	NaN	S
364	365	0	3	O’Brien, Mr. Thomas	male	NaN	1	0	370365	15.5	NaN	Q
365	366	0	3	Adahl, Mr. Mauritz Nils Martin	male	30.0	0	0	C 7076	7.25	NaN	S
366	367	1	1	Warren, Mrs. Frank Manley (Anna Sophia Atkinson)	female	60.0	1	0	110813	75.25	D37	C
367	368	1	3	Moussa, Mrs. (Mantoura Boulos)	female	NaN	0	0	2626	7.2292	NaN	C
368	369	1	3	Jermyn, Miss. Annie	female	NaN	0	0	14313	7.75	NaN	Q
369	370	1	1	Aubart, Mme. Leontine Pauline	female	24.0	0	0	PC 17477	69.3	B35	C
370	371	1	1	Harder, Mr. George Achilles	male	25.0	1	0	11765	55.4417	E50	C
371	372	0	3	Wiklund, Mr. Jakob Alfred	male	18.0	1	0	3101267	6.4958	NaN	S
372	373	0	3	Beavan, Mr. William Thomas	male	19.0	0	0	323951	8.05	NaN	S
373	374	0	1	Ringhini, Mr. Sante	male	22.0	0	0	PC 17760	135.6333	NaN	C
374	375	0	3	Palsson, Miss. Stina Viola	female	3.0	3	1	349909	21.075	NaN	S
375	376	1	1	Meyer, Mrs. Edgar Joseph (Leila Saks)	female	NaN	1	0	PC 17604	82.1708	NaN	C
376	377	1	3	Landergren, Miss. Aurora Adelia	female	22.0	0	0	C 7077	7.25	NaN	S
377	378	0	1	Widener, Mr. Harry Elkins	male	27.0	0	2	113503	211.5	C82	C
378	379	0	3	Betros, Mr. Tannous	male	20.0	0	0	2648	4.0125	NaN	C
379	380	0	3	Gustafsson, Mr. Karl Gideon	male	19.0	0	0	347069	7.775	NaN	S
380	381	1	1	Bidois, Miss. Rosalie	female	42.0	0	0	PC 17757	227.525	NaN	C
381	382	1	3	Nakid, Miss. Maria (“Mary”)	female	1.0	0	2	2653	15.7417	NaN	C
382	383	0	3	Tikkanen, Mr. Juho	male	32.0	0	0	STON/O 2. 3101293	7.925	NaN	S
383	384	1	1	Holverson, Mrs. Alexander Oskar (Mary Aline Towner)	female	35.0	1	0	113789	52.0	NaN	S
384	385	0	3	Plotcharsky, Mr. Vasil	male	NaN	0	0	349227	7.8958	NaN	S
385	386	0	2	Davies, Mr. Charles Henry	male	18.0	0	0	S.O.C. 14879	73.5	NaN	S
386	387	0	3	Goodwin, Master. Sidney Leonard	male	1.0	5	2	CA 2144	46.9	NaN	S
387	388	1	2	Buss, Miss. Kate	female	36.0	0	0	27849	13.0	NaN	S
388	389	0	3	Sadlier, Mr. Matthew	male	NaN	0	0	367655	7.7292	NaN	Q
389	390	1	2	Lehmann, Miss. Bertha	female	17.0	0	0	SC 1748	12.0	NaN	C
390	391	1	1	Carter, Mr. William Ernest	male	36.0	1	2	113760	120.0	B96 B98	S
391	392	1	3	Jansson, Mr. Carl Olof	male	21.0	0	0	350034	7.7958	NaN	S
392	393	0	3	Gustafsson, Mr. Johan Birger	male	28.0	2	0	3101277	7.925	NaN	S
393	394	1	1	Newell, Miss. Marjorie	female	23.0	1	0	35273	113.275	D36	C
394	395	1	3	Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)	female	24.0	0	2	PP 9549	16.7	G6	S
395	396	0	3	Johansson, Mr. Erik	male	22.0	0	0	350052	7.7958	NaN	S
396	397	0	3	Olsson, Miss. Elina	female	31.0	0	0	350407	7.8542	NaN	S
397	398	0	2	McKane, Mr. Peter David	male	46.0	0	0	28403	26.0	NaN	S
398	399	0	2	Pain, Dr. Alfred	male	23.0	0	0	244278	10.5	NaN	S
399	400	1	2	Trout, Mrs. William H (Jessie L)	female	28.0	0	0	240929	12.65	NaN	S
400	401	1	3	Niskanen, Mr. Juha	male	39.0	0	0	STON/O 2. 3101289	7.925	NaN	S
401	402	0	3	Adams, Mr. John	male	26.0	0	0	341826	8.05	NaN	S
402	403	0	3	Jussila, Miss. Mari Aina	female	21.0	1	0	4137	9.825	NaN	S
403	404	0	3	Hakkarainen, Mr. Pekka Pietari	male	28.0	1	0	STON/O2. 3101279	15.85	NaN	S
404	405	0	3	Oreskovic, Miss. Marija	female	20.0	0	0	315096	8.6625	NaN	S
405	406	0	2	Gale, Mr. Shadrach	male	34.0	1	0	28664	21.0	NaN	S
406	407	0	3	Widegren, Mr. Carl/Charles Peter	male	51.0	0	0	347064	7.75	NaN	S
407	408	1	2	Richards, Master. William Rowe	male	3.0	1	1	29106	18.75	NaN	S
408	409	0	3	Birkeland, Mr. Hans Martin Monsen	male	21.0	0	0	312992	7.775	NaN	S
409	410	0	3	Lefebre, Miss. Ida	female	NaN	3	1	4133	25.4667	NaN	S
410	411	0	3	Sdycoff, Mr. Todor	male	NaN	0	0	349222	7.8958	NaN	S
411	412	0	3	Hart, Mr. Henry	male	NaN	0	0	394140	6.8583	NaN	Q
412	413	1	1	Minahan, Miss. Daisy E	female	33.0	1	0	19928	90.0	C78	Q
413	414	0	2	Cunningham, Mr. Alfred Fleming	male	NaN	0	0	239853	0.0	NaN	S
414	415	1	3	Sundman, Mr. Johan Julian	male	44.0	0	0	STON/O 2. 3101269	7.925	NaN	S
415	416	0	3	Meek, Mrs. Thomas (Annie Louise Rowley)	female	NaN	0	0	343095	8.05	NaN	S
416	417	1	2	Drew, Mrs. James Vivian (Lulu Thorne Christian)	female	34.0	1	1	28220	32.5	NaN	S
417	418	1	2	Silven, Miss. Lyyli Karoliina	female	18.0	0	2	250652	13.0	NaN	S
418	419	0	2	Matthews, Mr. William John	male	30.0	0	0	28228	13.0	NaN	S
419	420	0	3	Van Impe, Miss. Catharina	female	10.0	0	2	345773	24.15	NaN	S
420	421	0	3	Gheorgheff, Mr. Stanio	male	NaN	0	0	349254	7.8958	NaN	C
421	422	0	3	Charters, Mr. David	male	21.0	0	0	A/5. 13032	7.7333	NaN	Q
422	423	0	3	Zimmerman, Mr. Leo	male	29.0	0	0	315082	7.875	NaN	S
423	424	0	3	Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)	female	28.0	1	1	347080	14.4	NaN	S
424	425	0	3	Rosblom, Mr. Viktor Richard	male	18.0	1	1	370129	20.2125	NaN	S
425	426	0	3	Wiseman, Mr. Phillippe	male	NaN	0	0	A/4. 34244	7.25	NaN	S
426	427	1	2	Clarke, Mrs. Charles V (Ada Maria Winfield)	female	28.0	1	0	2003	26.0	NaN	S
427	428	1	2	Phillips, Miss. Kate Florence (“Mrs Kate Louise Phillips Marshall”)	female	19.0	0	0	250655	26.0	NaN	S
428	429	0	3	Flynn, Mr. James	male	NaN	0	0	364851	7.75	NaN	Q
429	430	1	3	Pickard, Mr. Berk (Berk Trembisky)	male	32.0	0	0	SOTON/O.Q. 392078	8.05	E10	S
430	431	1	1	Bjornstrom-Steffansson, Mr. Mauritz Hakan	male	28.0	0	0	110564	26.55	C52	S
431	432	1	3	Thorneycroft, Mrs. Percival (Florence Kate White)	female	NaN	1	0	376564	16.1	NaN	S
432	433	1	2	Louch, Mrs. Charles Alexander (Alice Adelaide Slow)	female	42.0	1	0	SC/AH 3085	26.0	NaN	S
433	434	0	3	Kallio, Mr. Nikolai Erland	male	17.0	0	0	STON/O 2. 3101274	7.125	NaN	S
434	435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9	E44	S
435	436	1	1	Carter, Miss. Lucile Polk	female	14.0	1	2	113760	120.0	B96 B98	S
436	437	0	3	Ford, Miss. Doolina Margaret “Daisy”	female	21.0	2	2	W./C. 6608	34.375	NaN	S
437	438	1	2	Richards, Mrs. Sidney (Emily Hocking)	female	24.0	2	3	29106	18.75	NaN	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0	C23 C25 C27	S
439	440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.0	0	0	C.A. 18723	10.5	NaN	S
440	441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	female	45.0	1	1	F.C.C. 13529	26.25	NaN	S
441	442	0	3	Hampe, Mr. Leon	male	20.0	0	0	345769	9.5	NaN	S
442	443	0	3	Petterson, Mr. Johan Emil	male	25.0	1	0	347076	7.775	NaN	S
443	444	1	2	Reynaldo, Ms. Encarnacion	female	28.0	0	0	230434	13.0	NaN	S
444	445	1	3	Johannesen-Bratthammer, Mr. Bernt	male	NaN	0	0	65306	8.1125	NaN	S
445	446	1	1	Dodge, Master. Washington	male	4.0	0	2	33638	81.8583	A34	S
446	447	1	2	Mellinger, Miss. Madeleine Violet	female	13.0	0	1	250644	19.5	NaN	S
447	448	1	1	Seward, Mr. Frederic Kimber	male	34.0	0	0	113794	26.55	NaN	S
448	449	1	3	Baclini, Miss. Marie Catherine	female	5.0	2	1	2666	19.2583	NaN	C
449	450	1	1	Peuchen, Major. Arthur Godfrey	male	52.0	0	0	113786	30.5	C104	S
450	451	0	2	West, Mr. Edwy Arthur	male	36.0	1	2	C.A. 34651	27.75	NaN	S
451	452	0	3	Hagland, Mr. Ingvald Olai Olsen	male	NaN	1	0	65303	19.9667	NaN	S
452	453	0	1	Foreman, Mr. Benjamin Laventall	male	30.0	0	0	113051	27.75	C111	C
453	454	1	1	Goldenberg, Mr. Samuel L	male	49.0	1	0	17453	89.1042	C92	C
454	455	0	3	Peduzzi, Mr. Joseph	male	NaN	0	0	A/5 2817	8.05	NaN	S
455	456	1	3	Jalsevac, Mr. Ivan	male	29.0	0	0	349240	7.8958	NaN	C
456	457	0	1	Millet, Mr. Francis Davis	male	65.0	0	0	13509	26.55	E38	S
457	458	1	1	Kenyon, Mrs. Frederick R (Marion)	female	NaN	1	0	17464	51.8625	D21	S
458	459	1	2	Toomey, Miss. Ellen	female	50.0	0	0	F.C.C. 13531	10.5	NaN	S
459	460	0	3	O’Connor, Mr. Maurice	male	NaN	0	0	371060	7.75	NaN	Q
460	461	1	1	Anderson, Mr. Harry	male	48.0	0	0	19952	26.55	E12	S
461	462	0	3	Morley, Mr. William	male	34.0	0	0	364506	8.05	NaN	S
462	463	0	1	Gee, Mr. Arthur H	male	47.0	0	0	111320	38.5	E63	S
463	464	0	2	Milling, Mr. Jacob Christian	male	48.0	0	0	234360	13.0	NaN	S
464	465	0	3	Maisner, Mr. Simon	male	NaN	0	0	A/S 2816	8.05	NaN	S
465	466	0	3	Goncalves, Mr. Manuel Estanslas	male	38.0	0	0	SOTON/O.Q. 3101306	7.05	NaN	S
466	467	0	2	Campbell, Mr. William	male	NaN	0	0	239853	0.0	NaN	S
467	468	0	1	Smart, Mr. John Montgomery	male	56.0	0	0	113792	26.55	NaN	S
468	469	0	3	Scanlan, Mr. James	male	NaN	0	0	36209	7.725	NaN	Q
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75	2	1	2666	19.2583	NaN	C
470	471	0	3	Keefe, Mr. Arthur	male	NaN	0	0	323592	7.25	NaN	S
471	472	0	3	Cacic, Mr. Luka	male	38.0	0	0	315089	8.6625	NaN	S
472	473	1	2	West, Mrs. Edwy Arthur (Ada Mary Worth)	female	33.0	1	2	C.A. 34651	27.75	NaN	S
473	474	1	2	Jerwan, Mrs. Amin S (Marie Marthe Thuillard)	female	23.0	0	0	SC/AH Basle 541	13.7917	D	C
474	475	0	3	Strandberg, Miss. Ida Sofia	female	22.0	0	0	7553	9.8375	NaN	S
475	476	0	1	Clifford, Mr. George Quincy	male	NaN	0	0	110465	52.0	A14	S
476	477	0	2	Renouf, Mr. Peter Henry	male	34.0	1	0	31027	21.0	NaN	S
477	478	0	3	Braund, Mr. Lewis Richard	male	29.0	1	0	3460	7.0458	NaN	S
478	479	0	3	Karlsson, Mr. Nils August	male	22.0	0	0	350060	7.5208	NaN	S
479	480	1	3	Hirvonen, Miss. Hildur E	female	2.0	0	1	3101298	12.2875	NaN	S
480	481	0	3	Goodwin, Master. Harold Victor	male	9.0	5	2	CA 2144	46.9	NaN	S
481	482	0	2	Frost, Mr. Anthony Wood “Archie”	male	NaN	0	0	239854	0.0	NaN	S
482	483	0	3	Rouse, Mr. Richard Henry	male	50.0	0	0	A/5 3594	8.05	NaN	S
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0	0	0	4134	9.5875	NaN	S
484	485	1	1	Bishop, Mr. Dickinson H	male	25.0	1	0	11967	91.0792	B49	C
485	486	0	3	Lefebre, Miss. Jeannie	female	NaN	3	1	4133	25.4667	NaN	S
486	487	1	1	Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)	female	35.0	1	0	19943	90.0	C93	S
487	488	0	1	Kent, Mr. Edward Austin	male	58.0	0	0	11771	29.7	B37	C
488	489	0	3	Somerton, Mr. Francis William	male	30.0	0	0	A.5. 18509	8.05	NaN	S
489	490	1	3	Coutts, Master. Eden Leslie “Neville”	male	9.0	1	1	C.A. 37671	15.9	NaN	S
490	491	0	3	Hagland, Mr. Konrad Mathias Reiersen	male	NaN	1	0	65304	19.9667	NaN	S
491	492	0	3	Windelov, Mr. Einar	male	21.0	0	0	SOTON/OQ 3101317	7.25	NaN	S
492	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5	C30	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
494	495	0	3	Stanley, Mr. Edward Roland	male	21.0	0	0	A/4 45380	8.05	NaN	S
495	496	0	3	Yousseff, Mr. Gerious	male	NaN	0	0	2627	14.4583	NaN	C
496	497	1	1	Eustis, Miss. Elizabeth Mussey	female	54.0	1	0	36947	78.2667	D20	C
497	498	0	3	Shellard, Mr. Frederick William	male	NaN	0	0	C.A. 6212	15.1	NaN	S
498	499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0	1	2	113781	151.55	C22 C26	S
499	500	0	3	Svensson, Mr. Olof	male	24.0	0	0	350035	7.7958	NaN	S
500	501	0	3	Calic, Mr. Petar	male	17.0	0	0	315086	8.6625	NaN	S
501	502	0	3	Canavan, Miss. Mary	female	21.0	0	0	364846	7.75	NaN	Q
502	503	0	3	O’Sullivan, Miss. Bridget Mary	female	NaN	0	0	330909	7.6292	NaN	Q
503	504	0	3	Laitinen, Miss. Kristina Sofia	female	37.0	0	0	4135	9.5875	NaN	S
504	505	1	1	Maioni, Miss. Roberta	female	16.0	0	0	110152	86.5	B79	S
505	506	0	1	Penasco y Castellana, Mr. Victor de Satode	male	18.0	1	0	PC 17758	108.9	C65	C
506	507	1	2	Quick, Mrs. Frederick Charles (Jane Richards)	female	33.0	0	2	26360	26.0	NaN	S
507	508	1	1	Bradley, Mr. George (“George Arthur Brayton”)	male	NaN	0	0	111427	26.55	NaN	S
508	509	0	3	Olsen, Mr. Henry Margido	male	28.0	0	0	C 4001	22.525	NaN	S
509	510	1	3	Lang, Mr. Fang	male	26.0	0	0	1601	56.4958	NaN	S
510	511	1	3	Daly, Mr. Eugene Patrick	male	29.0	0	0	382651	7.75	NaN	Q
511	512	0	3	Webber, Mr. James	male	NaN	0	0	SOTON/OQ 3101316	8.05	NaN	S
512	513	1	1	McGough, Mr. James Robert	male	36.0	0	0	PC 17473	26.2875	E25	S
513	514	1	1	Rothschild, Mrs. Martin (Elizabeth L. Barrett)	female	54.0	1	0	PC 17603	59.4	NaN	C
514	515	0	3	Coleff, Mr. Satio	male	24.0	0	0	349209	7.4958	NaN	S
515	516	0	1	Walker, Mr. William Anderson	male	47.0	0	0	36967	34.0208	D46	S
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5	F33	S
517	518	0	3	Ryan, Mr. Patrick	male	NaN	0	0	371110	24.15	NaN	Q
518	519	1	2	Angle, Mrs. William A (Florence “Mary” Agnes Hughes)	female	36.0	1	0	226875	26.0	NaN	S
519	520	0	3	Pavlovic, Mr. Stefo	male	32.0	0	0	349242	7.8958	NaN	S
520	521	1	1	Perreault, Miss. Anne	female	30.0	0	0	12749	93.5	B73	S
521	522	0	3	Vovk, Mr. Janko	male	22.0	0	0	349252	7.8958	NaN	S
522	523	0	3	Lahoud, Mr. Sarkis	male	NaN	0	0	2624	7.225	NaN	C
523	524	1	1	Hippach, Mrs. Louis Albert (Ida Sophia Fischer)	female	44.0	0	1	111361	57.9792	B18	C
524	525	0	3	Kassem, Mr. Fared	male	NaN	0	0	2700	7.2292	NaN	C
525	526	0	3	Farrell, Mr. James	male	40.5	0	0	367232	7.75	NaN	Q
526	527	1	2	Ridsdale, Miss. Lucy	female	50.0	0	0	W./C. 14258	10.5	NaN	S
527	528	0	1	Farthing, Mr. John	male	NaN	0	0	PC 17483	221.7792	C95	S
528	529	0	3	Salonen, Mr. Johan Werner	male	39.0	0	0	3101296	7.925	NaN	S
529	530	0	2	Hocking, Mr. Richard George	male	23.0	2	1	29104	11.5	NaN	S
530	531	1	2	Quick, Miss. Phyllis May	female	2.0	1	1	26360	26.0	NaN	S
531	532	0	3	Toufik, Mr. Nakli	male	NaN	0	0	2641	7.2292	NaN	C
532	533	0	3	Elias, Mr. Joseph Jr	male	17.0	1	1	2690	7.2292	NaN	C
533	534	1	3	Peter, Mrs. Catherine (Catherine Rizk)	female	NaN	0	2	2668	22.3583	NaN	C
534	535	0	3	Cacic, Miss. Marija	female	30.0	0	0	315084	8.6625	NaN	S
535	536	1	2	Hart, Miss. Eva Miriam	female	7.0	0	2	F.C.C. 13529	26.25	NaN	S
536	537	0	1	Butt, Major. Archibald Willingham	male	45.0	0	0	113050	26.55	B38	S
537	538	1	1	LeRoy, Miss. Bertha	female	30.0	0	0	PC 17761	106.425	NaN	C
538	539	0	3	Risien, Mr. Samuel Beard	male	NaN	0	0	364498	14.5	NaN	S
539	540	1	1	Frolicher, Miss. Hedwig Margaritha	female	22.0	0	2	13568	49.5	B39	C
540	541	1	1	Crosby, Miss. Harriet R	female	36.0	0	2	WE/P 5735	71.0	B22	S
541	542	0	3	Andersson, Miss. Ingeborg Constanzia	female	9.0	4	2	347082	31.275	NaN	S
542	543	0	3	Andersson, Miss. Sigrid Elisabeth	female	11.0	4	2	347082	31.275	NaN	S
543	544	1	2	Beane, Mr. Edward	male	32.0	1	0	2908	26.0	NaN	S
544	545	0	1	Douglas, Mr. Walter Donald	male	50.0	1	0	PC 17761	106.425	C86	C
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.0	0	0	693	26.0	NaN	S
546	547	1	2	Beane, Mrs. Edward (Ethel Clarke)	female	19.0	1	0	2908	26.0	NaN	S
547	548	1	2	Padro y Manent, Mr. Julian	male	NaN	0	0	SC/PARIS 2146	13.8625	NaN	C
548	549	0	3	Goldsmith, Mr. Frank John	male	33.0	1	1	363291	20.525	NaN	S
549	550	1	2	Davies, Master. John Morgan Jr	male	8.0	1	1	C.A. 33112	36.75	NaN	S
550	551	1	1	Thayer, Mr. John Borland Jr	male	17.0	0	2	17421	110.8833	C70	C
551	552	0	2	Sharp, Mr. Percival James R	male	27.0	0	0	244358	26.0	NaN	S
552	553	0	3	O’Brien, Mr. Timothy	male	NaN	0	0	330979	7.8292	NaN	Q
553	554	1	3	Leeni, Mr. Fahim (“Philip Zenni”)	male	22.0	0	0	2620	7.225	NaN	C
554	555	1	3	Ohman, Miss. Velin	female	22.0	0	0	347085	7.775	NaN	S
555	556	0	1	Wright, Mr. George	male	62.0	0	0	113807	26.55	NaN	S
556	557	1	1	Duff Gordon, Lady. (Lucille Christiana Sutherland) (“Mrs Morgan”)	female	48.0	1	0	11755	39.6	A16	C
557	558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.525	NaN	C
558	559	1	1	Taussig, Mrs. Emil (Tillie Mandelbaum)	female	39.0	1	1	110413	79.65	E67	S
559	560	1	3	de Messemaeker, Mrs. Guillaume Joseph (Emma)	female	36.0	1	0	345572	17.4	NaN	S
560	561	0	3	Morrow, Mr. Thomas Rowan	male	NaN	0	0	372622	7.75	NaN	Q
561	562	0	3	Sivic, Mr. Husein	male	40.0	0	0	349251	7.8958	NaN	S
562	563	0	2	Norman, Mr. Robert Douglas	male	28.0	0	0	218629	13.5	NaN	S
563	564	0	3	Simmons, Mr. John	male	NaN	0	0	SOTON/OQ 392082	8.05	NaN	S
564	565	0	3	Meanwell, Miss. (Marion Ogden)	female	NaN	0	0	SOTON/O.Q. 392087	8.05	NaN	S
565	566	0	3	Davies, Mr. Alfred J	male	24.0	2	0	A/4 48871	24.15	NaN	S
566	567	0	3	Stoytcheff, Mr. Ilia	male	19.0	0	0	349205	7.8958	NaN	S
567	568	0	3	Palsson, Mrs. Nils (Alma Cornelia Berglund)	female	29.0	0	4	349909	21.075	NaN	S
568	569	0	3	Doharr, Mr. Tannous	male	NaN	0	0	2686	7.2292	NaN	C
569	570	1	3	Jonsson, Mr. Carl	male	32.0	0	0	350417	7.8542	NaN	S
570	571	1	2	Harris, Mr. George	male	62.0	0	0	S.W./PP 752	10.5	NaN	S
571	572	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53.0	2	0	11769	51.4792	C101	S
572	573	1	1	Flynn, Mr. John Irwin (“Irving”)	male	36.0	0	0	PC 17474	26.3875	E25	S
573	574	1	3	Kelly, Miss. Mary	female	NaN	0	0	14312	7.75	NaN	Q
574	575	0	3	Rush, Mr. Alfred George John	male	16.0	0	0	A/4. 20589	8.05	NaN	S
575	576	0	3	Patchett, Mr. George	male	19.0	0	0	358585	14.5	NaN	S
576	577	1	2	Garside, Miss. Ethel	female	34.0	0	0	243880	13.0	NaN	S
577	578	1	1	Silvey, Mrs. William Baird (Alice Munger)	female	39.0	1	0	13507	55.9	E44	S
578	579	0	3	Caram, Mrs. Joseph (Maria Elias)	female	NaN	1	0	2689	14.4583	NaN	C
579	580	1	3	Jussila, Mr. Eiriik	male	32.0	0	0	STON/O 2. 3101286	7.925	NaN	S
580	581	1	2	Christy, Miss. Julie Rachel	female	25.0	1	1	237789	30.0	NaN	S
581	582	1	1	Thayer, Mrs. John Borland (Marian Longstreth Morris)	female	39.0	1	1	17421	110.8833	C68	C
582	583	0	2	Downton, Mr. William James	male	54.0	0	0	28403	26.0	NaN	S
583	584	0	1	Ross, Mr. John Hugo	male	36.0	0	0	13049	40.125	A10	C
584	585	0	3	Paulner, Mr. Uscher	male	NaN	0	0	3411	8.7125	NaN	C
585	586	1	1	Taussig, Miss. Ruth	female	18.0	0	2	110413	79.65	E68	S
586	587	0	2	Jarvis, Mr. John Denzil	male	47.0	0	0	237565	15.0	NaN	S
587	588	1	1	Frolicher-Stehli, Mr. Maxmillian	male	60.0	1	1	13567	79.2	B41	C
588	589	0	3	Gilinski, Mr. Eliezer	male	22.0	0	0	14973	8.05	NaN	S
589	590	0	3	Murdlin, Mr. Joseph	male	NaN	0	0	A./5. 3235	8.05	NaN	S
590	591	0	3	Rintamaki, Mr. Matti	male	35.0	0	0	STON/O 2. 3101273	7.125	NaN	S
591	592	1	1	Stephenson, Mrs. Walter Bertram (Martha Eustis)	female	52.0	1	0	36947	78.2667	D20	C
592	593	0	3	Elsbury, Mr. William James	male	47.0	0	0	A/5 3902	7.25	NaN	S
593	594	0	3	Bourke, Miss. Mary	female	NaN	0	2	364848	7.75	NaN	Q
594	595	0	2	Chapman, Mr. John Henry	male	37.0	1	0	SC/AH 29037	26.0	NaN	S
595	596	0	3	Van Impe, Mr. Jean Baptiste	male	36.0	1	1	345773	24.15	NaN	S
596	597	1	2	Leitch, Miss. Jessie Wills	female	NaN	0	0	248727	33.0	NaN	S
597	598	0	3	Johnson, Mr. Alfred	male	49.0	0	0	LINE	0.0	NaN	S
598	599	0	3	Boulos, Mr. Hanna	male	NaN	0	0	2664	7.225	NaN	C
599	600	1	1	Duff Gordon, Sir. Cosmo Edmund (“Mr Morgan”)	male	49.0	1	0	PC 17485	56.9292	A20	C
600	601	1	2	Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)	female	24.0	2	1	243847	27.0	NaN	S
601	602	0	3	Slabenoff, Mr. Petco	male	NaN	0	0	349214	7.8958	NaN	S
602	603	0	1	Harrington, Mr. Charles H	male	NaN	0	0	113796	42.4	NaN	S
603	604	0	3	Torber, Mr. Ernst William	male	44.0	0	0	364511	8.05	NaN	S
604	605	1	1	Homer, Mr. Harry (“Mr E Haven”)	male	35.0	0	0	111426	26.55	NaN	C
605	606	0	3	Lindell, Mr. Edvard Bengtsson	male	36.0	1	0	349910	15.55	NaN	S
606	607	0	3	Karaic, Mr. Milan	male	30.0	0	0	349246	7.8958	NaN	S
607	608	1	1	Daniel, Mr. Robert Williams	male	27.0	0	0	113804	30.5	NaN	S
608	609	1	2	Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)	female	22.0	1	2	SC/Paris 2123	41.5792	NaN	C
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
610	611	0	3	Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)	female	39.0	1	5	347082	31.275	NaN	S
611	612	0	3	Jardin, Mr. Jose Neto	male	NaN	0	0	SOTON/O.Q. 3101305	7.05	NaN	S
612	613	1	3	Murphy, Miss. Margaret Jane	female	NaN	1	0	367230	15.5	NaN	Q
613	614	0	3	Horgan, Mr. John	male	NaN	0	0	370377	7.75	NaN	Q
614	615	0	3	Brocklebank, Mr. William Alfred	male	35.0	0	0	364512	8.05	NaN	S
615	616	1	2	Herman, Miss. Alice	female	24.0	1	2	220845	65.0	NaN	S
616	617	0	3	Danbom, Mr. Ernst Gilbert	male	34.0	1	1	347080	14.4	NaN	S
617	618	0	3	Lobb, Mrs. William Arthur (Cordelia K Stanlick)	female	26.0	1	0	A/5. 3336	16.1	NaN	S
618	619	1	2	Becker, Miss. Marion Louise	female	4.0	2	1	230136	39.0	F4	S
619	620	0	2	Gavey, Mr. Lawrence	male	26.0	0	0	31028	10.5	NaN	S
620	621	0	3	Yasbeck, Mr. Antoni	male	27.0	1	0	2659	14.4542	NaN	C
621	622	1	1	Kimball, Mr. Edwin Nelson Jr	male	42.0	1	0	11753	52.5542	D19	S
622	623	1	3	Nakid, Mr. Sahid	male	20.0	1	1	2653	15.7417	NaN	C
623	624	0	3	Hansen, Mr. Henry Damsgaard	male	21.0	0	0	350029	7.8542	NaN	S
624	625	0	3	Bowen, Mr. David John “Dai”	male	21.0	0	0	54636	16.1	NaN	S
625	626	0	1	Sutton, Mr. Frederick	male	61.0	0	0	36963	32.3208	D50	S
626	627	0	2	Kirkland, Rev. Charles Leonard	male	57.0	0	0	219533	12.35	NaN	Q
627	628	1	1	Longley, Miss. Gretchen Fiske	female	21.0	0	0	13502	77.9583	D9	S
628	629	0	3	Bostandyeff, Mr. Guentcho	male	26.0	0	0	349224	7.8958	NaN	S
629	630	0	3	O’Connell, Mr. Patrick D	male	NaN	0	0	334912	7.7333	NaN	Q
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0	A23	S
631	632	0	3	Lundahl, Mr. Johan Svensson	male	51.0	0	0	347743	7.0542	NaN	S
632	633	1	1	Stahelin-Maeglin, Dr. Max	male	32.0	0	0	13214	30.5	B50	C
633	634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0	NaN	S
634	635	0	3	Skoog, Miss. Mabel	female	9.0	3	2	347088	27.9	NaN	S
635	636	1	2	Davis, Miss. Mary	female	28.0	0	0	237668	13.0	NaN	S
636	637	0	3	Leinonen, Mr. Antti Gustaf	male	32.0	0	0	STON/O 2. 3101292	7.925	NaN	S
637	638	0	2	Collyer, Mr. Harvey	male	31.0	1	1	C.A. 31921	26.25	NaN	S
638	639	0	3	Panula, Mrs. Juha (Maria Emilia Ojala)	female	41.0	0	5	3101295	39.6875	NaN	S
639	640	0	3	Thorneycroft, Mr. Percival	male	NaN	1	0	376564	16.1	NaN	S
640	641	0	3	Jensen, Mr. Hans Peder	male	20.0	0	0	350050	7.8542	NaN	S
641	642	1	1	Sagesser, Mlle. Emma	female	24.0	0	0	PC 17477	69.3	B35	C
642	643	0	3	Skoog, Miss. Margit Elizabeth	female	2.0	3	2	347088	27.9	NaN	S
643	644	1	3	Foo, Mr. Choong	male	NaN	0	0	1601	56.4958	NaN	S
644	645	1	3	Baclini, Miss. Eugenie	female	0.75	2	1	2666	19.2583	NaN	C
645	646	1	1	Harper, Mr. Henry Sleeper	male	48.0	1	0	PC 17572	76.7292	D33	C
646	647	0	3	Cor, Mr. Liudevit	male	19.0	0	0	349231	7.8958	NaN	S
647	648	1	1	Simonius-Blumer, Col. Oberst Alfons	male	56.0	0	0	13213	35.5	A26	C
648	649	0	3	Willey, Mr. Edward	male	NaN	0	0	S.O./P.P. 751	7.55	NaN	S
649	650	1	3	Stanley, Miss. Amy Zillah Elsie	female	23.0	0	0	CA. 2314	7.55	NaN	S
650	651	0	3	Mitkoff, Mr. Mito	male	NaN	0	0	349221	7.8958	NaN	S
651	652	1	2	Doling, Miss. Elsie	female	18.0	0	1	231919	23.0	NaN	S
652	653	0	3	Kalvik, Mr. Johannes Halvorsen	male	21.0	0	0	8475	8.4333	NaN	S
653	654	1	3	O’Leary, Miss. Hanora “Norah”	female	NaN	0	0	330919	7.8292	NaN	Q
654	655	0	3	Hegarty, Miss. Hanora “Nora”	female	18.0	0	0	365226	6.75	NaN	Q
655	656	0	2	Hickman, Mr. Leonard Mark	male	24.0	2	0	S.O.C. 14879	73.5	NaN	S
656	657	0	3	Radeff, Mr. Alexander	male	NaN	0	0	349223	7.8958	NaN	S
657	658	0	3	Bourke, Mrs. John (Catherine)	female	32.0	1	1	364849	15.5	NaN	Q
658	659	0	2	Eitemiller, Mr. George Floyd	male	23.0	0	0	29751	13.0	NaN	S
659	660	0	1	Newell, Mr. Arthur Webster	male	58.0	0	2	35273	113.275	D48	C
660	661	1	1	Frauenthal, Dr. Henry William	male	50.0	2	0	PC 17611	133.65	NaN	S
661	662	0	3	Badt, Mr. Mohamed	male	40.0	0	0	2623	7.225	NaN	C
662	663	0	1	Colley, Mr. Edward Pomeroy	male	47.0	0	0	5727	25.5875	E58	S
663	664	0	3	Coleff, Mr. Peju	male	36.0	0	0	349210	7.4958	NaN	S
664	665	1	3	Lindqvist, Mr. Eino William	male	20.0	1	0	STON/O 2. 3101285	7.925	NaN	S
665	666	0	2	Hickman, Mr. Lewis	male	32.0	2	0	S.O.C. 14879	73.5	NaN	S
666	667	0	2	Butler, Mr. Reginald Fenton	male	25.0	0	0	234686	13.0	NaN	S
667	668	0	3	Rommetvedt, Mr. Knud Paust	male	NaN	0	0	312993	7.775	NaN	S
668	669	0	3	Cook, Mr. Jacob	male	43.0	0	0	A/5 3536	8.05	NaN	S
669	670	1	1	Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)	female	NaN	1	0	19996	52.0	C126	S
670	671	1	2	Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)	female	40.0	1	1	29750	39.0	NaN	S
671	672	0	1	Davidson, Mr. Thornton	male	31.0	1	0	F.C. 12750	52.0	B71	S
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0	0	0	C.A. 24580	10.5	NaN	S
673	674	1	2	Wilhelms, Mr. Charles	male	31.0	0	0	244270	13.0	NaN	S
674	675	0	2	Watson, Mr. Ennis Hastings	male	NaN	0	0	239856	0.0	NaN	S
675	676	0	3	Edvardsson, Mr. Gustaf Hjalmar	male	18.0	0	0	349912	7.775	NaN	S
676	677	0	3	Sawyer, Mr. Frederick Charles	male	24.5	0	0	342826	8.05	NaN	S
677	678	1	3	Turja, Miss. Anna Sofia	female	18.0	0	0	4138	9.8417	NaN	S
678	679	0	3	Goodwin, Mrs. Frederick (Augusta Tyler)	female	43.0	1	6	CA 2144	46.9	NaN	S
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
680	681	0	3	Peters, Miss. Katie	female	NaN	0	0	330935	8.1375	NaN	Q
681	682	1	1	Hassab, Mr. Hammad	male	27.0	0	0	PC 17572	76.7292	D49	C
682	683	0	3	Olsvigen, Mr. Thor Anderson	male	20.0	0	0	6563	9.225	NaN	S
683	684	0	3	Goodwin, Mr. Charles Edward	male	14.0	5	2	CA 2144	46.9	NaN	S
684	685	0	2	Brown, Mr. Thomas William Solomon	male	60.0	1	1	29750	39.0	NaN	S
685	686	0	2	Laroche, Mr. Joseph Philippe Lemercier	male	25.0	1	2	SC/Paris 2123	41.5792	NaN	C
686	687	0	3	Panula, Mr. Jaako Arnold	male	14.0	4	1	3101295	39.6875	NaN	S
687	688	0	3	Dakic, Mr. Branko	male	19.0	0	0	349228	10.1708	NaN	S
688	689	0	3	Fischer, Mr. Eberhard Thelander	male	18.0	0	0	350036	7.7958	NaN	S
689	690	1	1	Madill, Miss. Georgette Alexandra	female	15.0	0	1	24160	211.3375	B5	S
690	691	1	1	Dick, Mr. Albert Adrian	male	31.0	1	0	17474	57.0	B20	S
691	692	1	3	Karun, Miss. Manca	female	4.0	0	1	349256	13.4167	NaN	C
692	693	1	3	Lam, Mr. Ali	male	NaN	0	0	1601	56.4958	NaN	S
693	694	0	3	Saad, Mr. Khalil	male	25.0	0	0	2672	7.225	NaN	C
694	695	0	1	Weir, Col. John	male	60.0	0	0	113800	26.55	NaN	S
695	696	0	2	Chapman, Mr. Charles Henry	male	52.0	0	0	248731	13.5	NaN	S
696	697	0	3	Kelly, Mr. James	male	44.0	0	0	363592	8.05	NaN	S
697	698	1	3	Mullens, Miss. Katherine “Katie”	female	NaN	0	0	35852	7.7333	NaN	Q
698	699	0	1	Thayer, Mr. John Borland	male	49.0	1	1	17421	110.8833	C68	C
699	700	0	3	Humblen, Mr. Adolf Mathias Nicolai Olsen	male	42.0	0	0	348121	7.65	F G63	S
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0	1	0	PC 17757	227.525	C62 C64	C
701	702	1	1	Silverthorne, Mr. Spencer Victor	male	35.0	0	0	PC 17475	26.2875	E24	S
702	703	0	3	Barbara, Miss. Saiide	female	18.0	0	1	2691	14.4542	NaN	C
703	704	0	3	Gallagher, Mr. Martin	male	25.0	0	0	36864	7.7417	NaN	Q
704	705	0	3	Hansen, Mr. Henrik Juul	male	26.0	1	0	350025	7.8542	NaN	S
705	706	0	2	Morley, Mr. Henry Samuel (“Mr Henry Marshall”)	male	39.0	0	0	250655	26.0	NaN	S
706	707	1	2	Kelly, Mrs. Florence “Fannie”	female	45.0	0	0	223596	13.5	NaN	S
707	708	1	1	Calderhead, Mr. Edward Pennington	male	42.0	0	0	PC 17476	26.2875	E24	S
708	709	1	1	Cleaver, Miss. Alice	female	22.0	0	0	113781	151.55	NaN	S
709	710	1	3	Moubarek, Master. Halim Gonios (“William George”)	male	NaN	1	1	2661	15.2458	NaN	C
710	711	1	1	Mayne, Mlle. Berthe Antonine (“Mrs de Villiers”)	female	24.0	0	0	PC 17482	49.5042	C90	C
711	712	0	1	Klaber, Mr. Herman	male	NaN	0	0	113028	26.55	C124	S
712	713	1	1	Taylor, Mr. Elmer Zebley	male	48.0	1	0	19996	52.0	C126	S
713	714	0	3	Larsson, Mr. August Viktor	male	29.0	0	0	7545	9.4833	NaN	S
714	715	0	2	Greenberg, Mr. Samuel	male	52.0	0	0	250647	13.0	NaN	S
715	716	0	3	Soholt, Mr. Peter Andreas Lauritz Andersen	male	19.0	0	0	348124	7.65	F G73	S
716	717	1	1	Endres, Miss. Caroline Louise	female	38.0	0	0	PC 17757	227.525	C45	C
717	718	1	2	Troutt, Miss. Edwina Celia “Winnie”	female	27.0	0	0	34218	10.5	E101	S
718	719	0	3	McEvoy, Mr. Michael	male	NaN	0	0	36568	15.5	NaN	Q
719	720	0	3	Johnson, Mr. Malkolm Joackim	male	33.0	0	0	347062	7.775	NaN	S
720	721	1	2	Harper, Miss. Annie Jessie “Nina”	female	6.0	0	1	248727	33.0	NaN	S
721	722	0	3	Jensen, Mr. Svend Lauritz	male	17.0	1	0	350048	7.0542	NaN	S
722	723	0	2	Gillespie, Mr. William Henry	male	34.0	0	0	12233	13.0	NaN	S
723	724	0	2	Hodges, Mr. Henry Price	male	50.0	0	0	250643	13.0	NaN	S
724	725	1	1	Chambers, Mr. Norman Campbell	male	27.0	1	0	113806	53.1	E8	S
725	726	0	3	Oreskovic, Mr. Luka	male	20.0	0	0	315094	8.6625	NaN	S
726	727	1	2	Renouf, Mrs. Peter Henry (Lillian Jefferys)	female	30.0	3	0	31027	21.0	NaN	S
727	728	1	3	Mannion, Miss. Margareth	female	NaN	0	0	36866	7.7375	NaN	Q
728	729	0	2	Bryhl, Mr. Kurt Arnold Gottfrid	male	25.0	1	0	236853	26.0	NaN	S
729	730	0	3	Ilmakangas, Miss. Pieta Sofia	female	25.0	1	0	STON/O2. 3101271	7.925	NaN	S
730	731	1	1	Allen, Miss. Elisabeth Walton	female	29.0	0	0	24160	211.3375	B5	S
731	732	0	3	Hassan, Mr. Houssein G N	male	11.0	0	0	2699	18.7875	NaN	C
732	733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0	NaN	S
733	734	0	2	Berriman, Mr. William John	male	23.0	0	0	28425	13.0	NaN	S
734	735	0	2	Troupiansky, Mr. Moses Aaron	male	23.0	0	0	233639	13.0	NaN	S
735	736	0	3	Williams, Mr. Leslie	male	28.5	0	0	54636	16.1	NaN	S
736	737	0	3	Ford, Mrs. Edward (Margaret Ann Watson)	female	48.0	1	3	W./C. 6608	34.375	NaN	S
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
738	739	0	3	Ivanoff, Mr. Kanio	male	NaN	0	0	349201	7.8958	NaN	S
739	740	0	3	Nankoff, Mr. Minko	male	NaN	0	0	349218	7.8958	NaN	S
740	741	1	1	Hawksford, Mr. Walter James	male	NaN	0	0	16988	30.0	D45	S
741	742	0	1	Cavendish, Mr. Tyrell William	male	36.0	1	0	19877	78.85	C46	S
742	743	1	1	Ryerson, Miss. Susan Parker “Suzette”	female	21.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C
743	744	0	3	McNamee, Mr. Neal	male	24.0	1	0	376566	16.1	NaN	S
744	745	1	3	Stranden, Mr. Juho	male	31.0	0	0	STON/O 2. 3101288	7.925	NaN	S
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0	B22	S
746	747	0	3	Abbott, Mr. Rossmore Edward	male	16.0	1	1	C.A. 2673	20.25	NaN	S
747	748	1	2	Sinkkonen, Miss. Anna	female	30.0	0	0	250648	13.0	NaN	S
748	749	0	1	Marvin, Mr. Daniel Warner	male	19.0	1	0	113773	53.1	D30	S
749	750	0	3	Connaghton, Mr. Michael	male	31.0	0	0	335097	7.75	NaN	Q
750	751	1	2	Wells, Miss. Joan	female	4.0	1	1	29103	23.0	NaN	S
751	752	1	3	Moor, Master. Meier	male	6.0	0	1	392096	12.475	E121	S
752	753	0	3	Vande Velde, Mr. Johannes Joseph	male	33.0	0	0	345780	9.5	NaN	S
753	754	0	3	Jonkoff, Mr. Lalio	male	23.0	0	0	349204	7.8958	NaN	S
754	755	1	2	Herman, Mrs. Samuel (Jane Laver)	female	48.0	1	2	220845	65.0	NaN	S
755	756	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	250649	14.5	NaN	S
756	757	0	3	Carlsson, Mr. August Sigfrid	male	28.0	0	0	350042	7.7958	NaN	S
757	758	0	2	Bailey, Mr. Percy Andrew	male	18.0	0	0	29108	11.5	NaN	S
758	759	0	3	Theobald, Mr. Thomas Leonard	male	34.0	0	0	363294	8.05	NaN	S
759	760	1	1	Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)	female	33.0	0	0	110152	86.5	B77	S
760	761	0	3	Garfirth, Mr. John	male	NaN	0	0	358585	14.5	NaN	S
761	762	0	3	Nirva, Mr. Iisakki Antino Aijo	male	41.0	0	0	SOTON/O2 3101272	7.125	NaN	S
762	763	1	3	Barah, Mr. Hanna Assi	male	20.0	0	0	2663	7.2292	NaN	C
763	764	1	1	Carter, Mrs. William Ernest (Lucile Polk)	female	36.0	1	2	113760	120.0	B96 B98	S
764	765	0	3	Eklund, Mr. Hans Linus	male	16.0	0	0	347074	7.775	NaN	S
765	766	1	1	Hogeboom, Mrs. John C (Anna Andrews)	female	51.0	1	0	13502	77.9583	D11	S
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN	0	0	112379	39.6	NaN	C
767	768	0	3	Mangan, Miss. Mary	female	30.5	0	0	364850	7.75	NaN	Q
768	769	0	3	Moran, Mr. Daniel J	male	NaN	1	0	371110	24.15	NaN	Q
769	770	0	3	Gronnestad, Mr. Daniel Danielsen	male	32.0	0	0	8471	8.3625	NaN	S
770	771	0	3	Lievens, Mr. Rene Aime	male	24.0	0	0	345781	9.5	NaN	S
771	772	0	3	Jensen, Mr. Niels Peder	male	48.0	0	0	350047	7.8542	NaN	S
772	773	0	2	Mack, Mrs. (Mary)	female	57.0	0	0	S.O./P.P. 3	10.5	E77	S
773	774	0	3	Elias, Mr. Dibo	male	NaN	0	0	2674	7.225	NaN	C
774	775	1	2	Hocking, Mrs. Elizabeth (Eliza Needs)	female	54.0	1	3	29105	23.0	NaN	S
775	776	0	3	Myhrman, Mr. Pehr Fabian Oliver Malkolm	male	18.0	0	0	347078	7.75	NaN	S
776	777	0	3	Tobin, Mr. Roger	male	NaN	0	0	383121	7.75	F38	Q
777	778	1	3	Emanuel, Miss. Virginia Ethel	female	5.0	0	0	364516	12.475	NaN	S
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN	0	0	36865	7.7375	NaN	Q
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)	female	43.0	0	1	24160	211.3375	B3	S
780	781	1	3	Ayoub, Miss. Banoura	female	13.0	0	0	2687	7.2292	NaN	C
781	782	1	1	Dick, Mrs. Albert Adrian (Vera Gillespie)	female	17.0	1	0	17474	57.0	B20	S
782	783	0	1	Long, Mr. Milton Clyde	male	29.0	0	0	113501	30.0	D6	S
783	784	0	3	Johnston, Mr. Andrew G	male	NaN	1	2	W./C. 6607	23.45	NaN	S
784	785	0	3	Ali, Mr. William	male	25.0	0	0	SOTON/O.Q. 3101312	7.05	NaN	S
785	786	0	3	Harmer, Mr. Abraham (David Lishin)	male	25.0	0	0	374887	7.25	NaN	S
786	787	1	3	Sjoblom, Miss. Anna Sofia	female	18.0	0	0	3101265	7.4958	NaN	S
787	788	0	3	Rice, Master. George Hugh	male	8.0	4	1	382652	29.125	NaN	Q
788	789	1	3	Dean, Master. Bertram Vere	male	1.0	1	2	C.A. 2315	20.575	NaN	S
789	790	0	1	Guggenheim, Mr. Benjamin	male	46.0	0	0	PC 17593	79.2	B82 B84	C
790	791	0	3	Keane, Mr. Andrew “Andy”	male	NaN	0	0	12460	7.75	NaN	Q
791	792	0	2	Gaskell, Mr. Alfred	male	16.0	0	0	239865	26.0	NaN	S
792	793	0	3	Sage, Miss. Stella Anna	female	NaN	8	2	CA. 2343	69.55	NaN	S
793	794	0	1	Hoyt, Mr. William Fisher	male	NaN	0	0	PC 17600	30.6958	NaN	C
794	795	0	3	Dantcheff, Mr. Ristiu	male	25.0	0	0	349203	7.8958	NaN	S
795	796	0	2	Otter, Mr. Richard	male	39.0	0	0	28213	13.0	NaN	S
796	797	1	1	Leader, Dr. Alice (Farnham)	female	49.0	0	0	17465	25.9292	D17	S
797	798	1	3	Osman, Mrs. Mara	female	31.0	0	0	349244	8.6833	NaN	S
798	799	0	3	Ibrahim Shawah, Mr. Yousseff	male	30.0	0	0	2685	7.2292	NaN	C
799	800	0	3	Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)	female	30.0	1	1	345773	24.15	NaN	S
800	801	0	2	Ponesell, Mr. Martin	male	34.0	0	0	250647	13.0	NaN	S
801	802	1	2	Collyer, Mrs. Harvey (Charlotte Annie Tate)	female	31.0	1	1	C.A. 31921	26.25	NaN	S
802	803	1	1	Carter, Master. William Thornton II	male	11.0	1	2	113760	120.0	B96 B98	S
803	804	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	2625	8.5167	NaN	C
804	805	1	3	Hedman, Mr. Oskar Arvid	male	27.0	0	0	347089	6.975	NaN	S
805	806	0	3	Johansson, Mr. Karl Johan	male	31.0	0	0	347063	7.775	NaN	S
806	807	0	1	Andrews, Mr. Thomas Jr	male	39.0	0	0	112050	0.0	A36	S
807	808	0	3	Pettersson, Miss. Ellen Natalia	female	18.0	0	0	347087	7.775	NaN	S
808	809	0	2	Meyer, Mr. August	male	39.0	0	0	248723	13.0	NaN	S
809	810	1	1	Chambers, Mrs. Norman Campbell (Bertha Griggs)	female	33.0	1	0	113806	53.1	E8	S
810	811	0	3	Alexander, Mr. William	male	26.0	0	0	3474	7.8875	NaN	S
811	812	0	3	Lester, Mr. James	male	39.0	0	0	A/4 48871	24.15	NaN	S
812	813	0	2	Slemen, Mr. Richard James	male	35.0	0	0	28206	10.5	NaN	S
813	814	0	3	Andersson, Miss. Ebba Iris Alfrida	female	6.0	4	2	347082	31.275	NaN	S
814	815	0	3	Tomlin, Mr. Ernest Portage	male	30.5	0	0	364499	8.05	NaN	S
815	816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0	B102	S
816	817	0	3	Heininen, Miss. Wendla Maria	female	23.0	0	0	STON/O2. 3101290	7.925	NaN	S
817	818	0	2	Mallet, Mr. Albert	male	31.0	1	1	S.C./PARIS 2079	37.0042	NaN	C
818	819	0	3	Holm, Mr. John Fredrik Alexander	male	43.0	0	0	C 7075	6.45	NaN	S
819	820	0	3	Skoog, Master. Karl Thorsten	male	10.0	3	2	347088	27.9	NaN	S
820	821	1	1	Hays, Mrs. Charles Melville (Clara Jennings Gregg)	female	52.0	1	1	12749	93.5	B69	S
821	822	1	3	Lulic, Mr. Nikola	male	27.0	0	0	315098	8.6625	NaN	S
822	823	0	1	Reuchlin, Jonkheer. John George	male	38.0	0	0	19972	0.0	NaN	S
823	824	1	3	Moor, Mrs. (Beila)	female	27.0	0	1	392096	12.475	E121	S
824	825	0	3	Panula, Master. Urho Abraham	male	2.0	4	1	3101295	39.6875	NaN	S
825	826	0	3	Flynn, Mr. John	male	NaN	0	0	368323	6.95	NaN	Q
826	827	0	3	Lam, Mr. Len	male	NaN	0	0	1601	56.4958	NaN	S
827	828	1	2	Mallet, Master. Andre	male	1.0	0	2	S.C./PARIS 2079	37.0042	NaN	C
828	829	1	3	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.75	NaN	Q
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN
830	831	1	3	Yasbeck, Mrs. Antoni (Selini Alexander)	female	15.0	1	0	2659	14.4542	NaN	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.75	NaN	S
832	833	0	3	Saad, Mr. Amin	male	NaN	0	0	2671	7.2292	NaN	C
833	834	0	3	Augustsson, Mr. Albert	male	23.0	0	0	347468	7.8542	NaN	S
834	835	0	3	Allum, Mr. Owen George	male	18.0	0	0	2223	8.3	NaN	S
835	836	1	1	Compton, Miss. Sara Rebecca	female	39.0	1	1	PC 17756	83.1583	E49	C
836	837	0	3	Pasic, Mr. Jakob	male	21.0	0	0	315097	8.6625	NaN	S
837	838	0	3	Sirota, Mr. Maurice	male	NaN	0	0	392092	8.05	NaN	S
838	839	1	3	Chip, Mr. Chang	male	32.0	0	0	1601	56.4958	NaN	S
839	840	1	1	Marechal, Mr. Pierre	male	NaN	0	0	11774	29.7	C47	C
840	841	0	3	Alhomaki, Mr. Ilmari Rudolf	male	20.0	0	0	SOTON/O2 3101287	7.925	NaN	S
841	842	0	2	Mudd, Mr. Thomas Charles	male	16.0	0	0	S.O./P.P. 3	10.5	NaN	S
842	843	1	1	Serepeca, Miss. Augusta	female	30.0	0	0	113798	31.0	NaN	C
843	844	0	3	Lemberopolous, Mr. Peter L	male	34.5	0	0	2683	6.4375	NaN	C
844	845	0	3	Culumovic, Mr. Jeso	male	17.0	0	0	315090	8.6625	NaN	S
845	846	0	3	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	7.55	NaN	S
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN	8	2	CA. 2343	69.55	NaN	S
847	848	0	3	Markoff, Mr. Marin	male	35.0	0	0	349213	7.8958	NaN	C
848	849	0	2	Harper, Rev. John	male	28.0	0	1	248727	33.0	NaN	S
849	850	1	1	Goldenberg, Mrs. Samuel L (Edwiga Grabowska)	female	NaN	1	0	17453	89.1042	C92	C
850	851	0	3	Andersson, Master. Sigvard Harald Elias	male	4.0	4	2	347082	31.275	NaN	S
851	852	0	3	Svensson, Mr. Johan	male	74.0	0	0	347060	7.775	NaN	S
852	853	0	3	Boulos, Miss. Nourelain	female	9.0	1	1	2678	15.2458	NaN	C
853	854	1	1	Lines, Miss. Mary Conover	female	16.0	0	1	PC 17592	39.4	D28	S
854	855	0	2	Carter, Mrs. Ernest Courtenay (Lilian Hughes)	female	44.0	1	0	244252	26.0	NaN	S
855	856	1	3	Aks, Mrs. Sam (Leah Rosen)	female	18.0	0	1	392091	9.35	NaN	S
856	857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.0	1	1	36928	164.8667	NaN	S
857	858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.55	E17	S
858	859	1	3	Baclini, Mrs. Solomon (Latifa Qurban)	female	24.0	0	3	2666	19.2583	NaN	C
859	860	0	3	Razi, Mr. Raihed	male	NaN	0	0	2629	7.2292	NaN	C
860	861	0	3	Hansen, Mr. Claus Peter	male	41.0	2	0	350026	14.1083	NaN	S
861	862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5	NaN	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Barron)	female	48.0	0	0	17466	25.9292	D17	S
863	864	0	3	Sage, Miss. Dorothy Edith “Dolly”	female	NaN	8	2	CA. 2343	69.55	NaN	S
864	865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0	NaN	S
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0	NaN	S
866	867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5	NaN	S
869	870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
870	871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0	B51 B53 B55	S
873	874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0	NaN	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0	NaN	C
875	876	1	3	Najib, Miss. Adele Kiamie “Jane”	female	15.0	0	0	2667	7.225	NaN	C
876	877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
877	878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
878	879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0	NaN	S
881	882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
882	883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
883	884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5	NaN	S
884	885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.05	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.125	NaN	Q
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen “Carrie”	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

Data exploration examples

In short, data exploration is an iterative process that helps to better understand the data and identify potential issues or patterns that may be relevant for further analysis. Some everyday tasks that are performed during data exploration include:

Examining the distribution of values for each variable
Identifying any missing or incomplete data
Detecting outliers or unusual values
Calculating summary statistics such as mean, median, and standard deviation
Visualizing relationships between variables using plots such as scatterplots or histograms.

Next, we will see the most useful data exploration functions in Pandas that are commonly used in data science and analysis.

The head() and tail()

The head() and tail() functions allow you to view the first or last few rows of a DataFrame, which can help get a feel for the data and identify any issues or patterns.

titanic_df.head()

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.25	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.925	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.05	NaN	S

titanic_df.tail()

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen “Carrie”	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

The describe()

The describe() function calculates a set of summary statistics for each column in a DataFrame, including the count, mean, median, standard deviation, and quartiles. It can be a quick and easy way to get a dataset summary.

titanic_df.describe()

index	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.0	891.0	891.0	714.0	891.0	891.0	891.0
mean	446.0	0.34	2.31	29.70	0.52	0.38	32.20
std	257.35	0.49	0.84	14.53	1.10	0.81	49.69
min	1.0	0.0	1.0	0.42	0.0	0.0	0.0
25%	223.5	0.0	2.0	20.13	0.0	0.0	7.91
50%	446.0	0.0	3.0	28.0	0.0	0.0	14.46
75%	668.5	1.0	3.0	38.0	1.0	0.0	31.0
max	891.0	1.0	3.0	80.0	8.0	6.0	512.33

The value_counts()

The value_counts() function counts the number of occurrences of each unique value in a Pandas Series (i.e., a single column of a DataFrame). It can be used to identify the most common values in a column and detect any unusual or unexpected ones. For instance, we can see passenger age distribution with value_counts() as follows and observe that the largest age group is people 24 years old.

titanic_df["Age"].value_counts(ascending=True)

74.00     1
14.50     1
70.50     1
12.00     1
36.50     1
         ..
30.00    25
19.00    25
18.00    26
22.00    27
24.00    30
Name: Age, Length: 88, dtype: int64

The corr()

The corr() function calculates the correlation between pairs of columns in a DataFrame. It can be used to identify relationships between variables and detect any multicollinearity.

titanic_df.corr()

index	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
PassengerId	1.0	-0.01	-0.04	0.04	-0.06	-0.0	0.01
Survived	-0.01	1.0	-0.34	-0.08	-0.04	0.08	0.26
Pclass	-0.04	-0.34	1.0	-0.37	0.08	0.02	-0.55
Age	0.04	-0.08	-0.37	1.0	-0.31	-0.19	0.1
SibSp	-0.06	-0.04	0.08	-0.31	1.0	0.41	0.16
Parch	-0.0	0.08	0.02	-0.19	0.41	1.0	0.22
Fare	0.01	0.26	-0.55	0.1	0.16	0.22	1.0

The info()

The info() function provides a summary of a Pandas DataFrame, including the number of rows and columns, the data type of each column, and the number of non-missing values in each column. It can help get a high-level overview of a dataset and identify any potential data issues or problems.

titanic_df.info()

RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  -----------  --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

Data Cleansing

Finding missing values

Real-life data often contains missing values that require removal or preprocessing. The simplest way to find missing values represented by NaN (which means “Not A Number”), which is a particular floating-point value and cannot be converted to any other type than float, is by using
.isna().any() and .isna().sum() functions. For null values, we similarly use isnull() function.

Firstly, let’s check the size of the Titanic dataset using the built-in shape sequence returning the number of rows and columns in the dataframe.

# The size of loaded dataset
titanic_df.shape

(891, 12)

# Finding all columns with NaN values:
titanic_df.isna().any()

PassengerId    False
Survived       False
Pclass         False
Name           False
Sex            False
Age             True
SibSp          False
Parch          False
Ticket         False
Fare           False
Cabin           True
Embarked        True
dtype: bool

With the sum() function, we see the total number of rows with the NaN values for each column.

titanic_df.isna().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

Alternatively, we can select all columns with NaNs as follow.

# Selecting all columns with NaN values
titanic_df[titanic_df.columns[titanic_df.isna().any()]]

index	Age	Cabin	Embarked
0	22.0	NaN	S
1	38.0	C85	C
2	26.0	NaN	S
3	35.0	C123	S
4	35.0	NaN	S
5	NaN	NaN	Q
6	54.0	E46	S
7	2.0	NaN	S
8	27.0	NaN	S
9	14.0	NaN	C
10	4.0	G6	S
11	58.0	C103	S
12	20.0	NaN	S
13	39.0	NaN	S
14	14.0	NaN	S
15	55.0	NaN	S
16	2.0	NaN	Q
17	NaN	NaN	S
18	31.0	NaN	S
19	NaN	NaN	C
20	35.0	NaN	S
21	34.0	D56	S
22	15.0	NaN	Q
23	28.0	A6	S
24	8.0	NaN	S
25	38.0	NaN	S
26	NaN	NaN	C
27	19.0	C23 C25 C27	S
28	NaN	NaN	Q
29	NaN	NaN	S
30	40.0	NaN	C
31	NaN	B78	C
32	NaN	NaN	Q
33	66.0	NaN	S
34	28.0	NaN	C
35	42.0	NaN	S
36	NaN	NaN	C
37	21.0	NaN	S
38	18.0	NaN	S
39	14.0	NaN	C
40	40.0	NaN	S
41	27.0	NaN	S
42	NaN	NaN	C
43	3.0	NaN	C
44	19.0	NaN	Q
45	NaN	NaN	S
46	NaN	NaN	Q
47	NaN	NaN	Q
48	NaN	NaN	C
49	18.0	NaN	S
50	7.0	NaN	S
51	21.0	NaN	S
52	49.0	D33	C
53	29.0	NaN	S
54	65.0	B30	C
55	NaN	C52	S
56	21.0	NaN	S
57	28.5	NaN	C
58	5.0	NaN	S
59	11.0	NaN	S
60	22.0	NaN	C
61	38.0	B28	NaN
62	45.0	C83	S
63	4.0	NaN	S
64	NaN	NaN	C
65	NaN	NaN	C
66	29.0	F33	S
67	19.0	NaN	S
68	17.0	NaN	S
69	26.0	NaN	S
70	32.0	NaN	S
71	16.0	NaN	S
72	21.0	NaN	S
73	26.0	NaN	C
74	32.0	NaN	S
75	25.0	F G73	S
76	NaN	NaN	S
77	NaN	NaN	S
78	0.83	NaN	S
79	30.0	NaN	S
80	22.0	NaN	S
81	29.0	NaN	S
82	NaN	NaN	Q
83	28.0	NaN	S
84	17.0	NaN	S
85	33.0	NaN	S
86	16.0	NaN	S
87	NaN	NaN	S
88	23.0	C23 C25 C27	S
89	24.0	NaN	S
90	29.0	NaN	S
91	20.0	NaN	S
92	46.0	E31	S
93	26.0	NaN	S
94	59.0	NaN	S
95	NaN	NaN	S
96	71.0	A5	C
97	23.0	D10 D12	C
98	34.0	NaN	S
99	34.0	NaN	S
100	28.0	NaN	S
101	NaN	NaN	S
102	21.0	D26	S
103	33.0	NaN	S
104	37.0	NaN	S
105	28.0	NaN	S
106	21.0	NaN	S
107	NaN	NaN	S
108	38.0	NaN	S
109	NaN	NaN	Q
110	47.0	C110	S
111	14.5	NaN	C
112	22.0	NaN	S
113	20.0	NaN	S
114	17.0	NaN	C
115	21.0	NaN	S
116	70.5	NaN	Q
117	29.0	NaN	S
118	24.0	B58 B60	C
119	2.0	NaN	S
120	21.0	NaN	S
121	NaN	NaN	S
122	32.5	NaN	C
123	32.5	E101	S
124	54.0	D26	S
125	12.0	NaN	C
126	NaN	NaN	Q
127	24.0	NaN	S
128	NaN	F E69	C
129	45.0	NaN	S
130	33.0	NaN	C
131	20.0	NaN	S
132	47.0	NaN	S
133	29.0	NaN	S
134	25.0	NaN	S
135	23.0	NaN	C
136	19.0	D47	S
137	37.0	C123	S
138	16.0	NaN	S
139	24.0	B86	C
140	NaN	NaN	C
141	22.0	NaN	S
142	24.0	NaN	S
143	19.0	NaN	Q
144	18.0	NaN	S
145	19.0	NaN	S
146	27.0	NaN	S
147	9.0	NaN	S
148	36.5	F2	S
149	42.0	NaN	S
150	51.0	NaN	S
151	22.0	C2	S
152	55.5	NaN	S
153	40.5	NaN	S
154	NaN	NaN	S
155	51.0	NaN	C
156	16.0	NaN	Q
157	30.0	NaN	S
158	NaN	NaN	S
159	NaN	NaN	S
160	44.0	NaN	S
161	40.0	NaN	S
162	26.0	NaN	S
163	17.0	NaN	S
164	1.0	NaN	S
165	9.0	NaN	S
166	NaN	E33	S
167	45.0	NaN	S
168	NaN	NaN	S
169	28.0	NaN	S
170	61.0	B19	S
171	4.0	NaN	Q
172	1.0	NaN	S
173	21.0	NaN	S
174	56.0	A7	C
175	18.0	NaN	S
176	NaN	NaN	S
177	50.0	C49	C
178	30.0	NaN	S
179	36.0	NaN	S
180	NaN	NaN	S
181	NaN	NaN	C
182	9.0	NaN	S
183	1.0	F4	S
184	4.0	NaN	S
185	NaN	A32	S
186	NaN	NaN	Q
187	45.0	NaN	S
188	40.0	NaN	Q
189	36.0	NaN	S
190	32.0	NaN	S
191	19.0	NaN	S
192	19.0	NaN	S
193	3.0	F2	S
194	44.0	B4	C
195	58.0	B80	C
196	NaN	NaN	Q
197	42.0	NaN	S
198	NaN	NaN	Q
199	24.0	NaN	S
200	28.0	NaN	S
201	NaN	NaN	S
202	34.0	NaN	S
203	45.5	NaN	C
204	18.0	NaN	S
205	2.0	G6	S
206	32.0	NaN	S
207	26.0	NaN	C
208	16.0	NaN	Q
209	40.0	A31	C
210	24.0	NaN	S
211	35.0	NaN	S
212	22.0	NaN	S
213	30.0	NaN	S
214	NaN	NaN	Q
215	31.0	D36	C
216	27.0	NaN	S
217	42.0	NaN	S
218	32.0	D15	C
219	30.0	NaN	S
220	16.0	NaN	S
221	27.0	NaN	S
222	51.0	NaN	S
223	NaN	NaN	S
224	38.0	C93	S
225	22.0	NaN	S
226	19.0	NaN	S
227	20.5	NaN	S
228	18.0	NaN	S
229	NaN	NaN	S
230	35.0	C83	S
231	29.0	NaN	S
232	59.0	NaN	S
233	5.0	NaN	S
234	24.0	NaN	S
235	NaN	NaN	S
236	44.0	NaN	S
237	8.0	NaN	S
238	19.0	NaN	S
239	33.0	NaN	S
240	NaN	NaN	C
241	NaN	NaN	Q
242	29.0	NaN	S
243	22.0	NaN	S
244	30.0	NaN	C
245	44.0	C78	Q
246	25.0	NaN	S
247	24.0	NaN	S
248	37.0	D35	S
249	54.0	NaN	S
250	NaN	NaN	S
251	29.0	G6	S
252	62.0	C87	S
253	30.0	NaN	S
254	41.0	NaN	S
255	29.0	NaN	C
256	NaN	NaN	C
257	30.0	B77	S
258	35.0	NaN	C
259	50.0	NaN	S
260	NaN	NaN	Q
261	3.0	NaN	S
262	52.0	E67	S
263	40.0	B94	S
264	NaN	NaN	Q
265	36.0	NaN	S
266	16.0	NaN	S
267	25.0	NaN	S
268	58.0	C125	S
269	35.0	C99	S
270	NaN	NaN	S
271	25.0	NaN	S
272	41.0	NaN	S
273	37.0	C118	C
274	NaN	NaN	Q
275	63.0	D7	S
276	45.0	NaN	S
277	NaN	NaN	S
278	7.0	NaN	Q
279	35.0	NaN	S
280	65.0	NaN	Q
281	28.0	NaN	S
282	16.0	NaN	S
283	19.0	NaN	S
284	NaN	A19	S
285	33.0	NaN	C
286	30.0	NaN	S
287	22.0	NaN	S
288	42.0	NaN	S
289	22.0	NaN	Q
290	26.0	NaN	S
291	19.0	B49	C
292	36.0	D	C
293	24.0	NaN	S
294	24.0	NaN	S
295	NaN	NaN	C
296	23.5	NaN	C
297	2.0	C22 C26	S
298	NaN	C106	S
299	50.0	B58 B60	C
300	NaN	NaN	Q
301	NaN	NaN	Q
302	19.0	NaN	S
303	NaN	E101	Q
304	NaN	NaN	S
305	0.92	C22 C26	S
306	NaN	NaN	C
307	17.0	C65	C
308	30.0	NaN	C
309	30.0	E36	C
310	24.0	C54	C
311	18.0	B57 B59 B63 B66	C
312	26.0	NaN	S
313	28.0	NaN	S
314	43.0	NaN	S
315	26.0	NaN	S
316	24.0	NaN	S
317	54.0	NaN	S
318	31.0	C7	S
319	40.0	E34	C
320	22.0	NaN	S
321	27.0	NaN	S
322	30.0	NaN	Q
323	22.0	NaN	S
324	NaN	NaN	S
325	36.0	C32	C
326	61.0	NaN	S
327	36.0	D	S
328	31.0	NaN	S
329	16.0	B18	C
330	NaN	NaN	Q
331	45.5	C124	S
332	38.0	C91	S
333	16.0	NaN	S
334	NaN	NaN	S
335	NaN	NaN	S
336	29.0	C2	S
337	41.0	E40	C
338	45.0	NaN	S
339	45.0	T	S
340	2.0	F2	S
341	24.0	C23 C25 C27	S
342	28.0	NaN	S
343	25.0	NaN	S
344	36.0	NaN	S
345	24.0	F33	S
346	40.0	NaN	S
347	NaN	NaN	S
348	3.0	NaN	S
349	42.0	NaN	S
350	23.0	NaN	S
351	NaN	C128	S
352	15.0	NaN	C
353	25.0	NaN	S
354	NaN	NaN	C
355	28.0	NaN	S
356	22.0	E33	S
357	38.0	NaN	S
358	NaN	NaN	Q
359	NaN	NaN	Q
360	40.0	NaN	S
361	29.0	NaN	C
362	45.0	NaN	C
363	35.0	NaN	S
364	NaN	NaN	Q
365	30.0	NaN	S
366	60.0	D37	C
367	NaN	NaN	C
368	NaN	NaN	Q
369	24.0	B35	C
370	25.0	E50	C
371	18.0	NaN	S
372	19.0	NaN	S
373	22.0	NaN	C
374	3.0	NaN	S
375	NaN	NaN	C
376	22.0	NaN	S
377	27.0	C82	C
378	20.0	NaN	C
379	19.0	NaN	S
380	42.0	NaN	C
381	1.0	NaN	C
382	32.0	NaN	S
383	35.0	NaN	S
384	NaN	NaN	S
385	18.0	NaN	S
386	1.0	NaN	S
387	36.0	NaN	S
388	NaN	NaN	Q
389	17.0	NaN	C
390	36.0	B96 B98	S
391	21.0	NaN	S
392	28.0	NaN	S
393	23.0	D36	C
394	24.0	G6	S
395	22.0	NaN	S
396	31.0	NaN	S
397	46.0	NaN	S
398	23.0	NaN	S
399	28.0	NaN	S
400	39.0	NaN	S
401	26.0	NaN	S
402	21.0	NaN	S
403	28.0	NaN	S
404	20.0	NaN	S
405	34.0	NaN	S
406	51.0	NaN	S
407	3.0	NaN	S
408	21.0	NaN	S
409	NaN	NaN	S
410	NaN	NaN	S
411	NaN	NaN	Q
412	33.0	C78	Q
413	NaN	NaN	S
414	44.0	NaN	S
415	NaN	NaN	S
416	34.0	NaN	S
417	18.0	NaN	S
418	30.0	NaN	S
419	10.0	NaN	S
420	NaN	NaN	C
421	21.0	NaN	Q
422	29.0	NaN	S
423	28.0	NaN	S
424	18.0	NaN	S
425	NaN	NaN	S
426	28.0	NaN	S
427	19.0	NaN	S
428	NaN	NaN	Q
429	32.0	E10	S
430	28.0	C52	S
431	NaN	NaN	S
432	42.0	NaN	S
433	17.0	NaN	S
434	50.0	E44	S
435	14.0	B96 B98	S
436	21.0	NaN	S
437	24.0	NaN	S
438	64.0	C23 C25 C27	S
439	31.0	NaN	S
440	45.0	NaN	S
441	20.0	NaN	S
442	25.0	NaN	S
443	28.0	NaN	S
444	NaN	NaN	S
445	4.0	A34	S
446	13.0	NaN	S
447	34.0	NaN	S
448	5.0	NaN	C
449	52.0	C104	S
450	36.0	NaN	S
451	NaN	NaN	S
452	30.0	C111	C
453	49.0	C92	C
454	NaN	NaN	S
455	29.0	NaN	C
456	65.0	E38	S
457	NaN	D21	S
458	50.0	NaN	S
459	NaN	NaN	Q
460	48.0	E12	S
461	34.0	NaN	S
462	47.0	E63	S
463	48.0	NaN	S
464	NaN	NaN	S
465	38.0	NaN	S
466	NaN	NaN	S
467	56.0	NaN	S
468	NaN	NaN	Q
469	0.75	NaN	C
470	NaN	NaN	S
471	38.0	NaN	S
472	33.0	NaN	S
473	23.0	D	C
474	22.0	NaN	S
475	NaN	A14	S
476	34.0	NaN	S
477	29.0	NaN	S
478	22.0	NaN	S
479	2.0	NaN	S
480	9.0	NaN	S
481	NaN	NaN	S
482	50.0	NaN	S
483	63.0	NaN	S
484	25.0	B49	C
485	NaN	NaN	S
486	35.0	C93	S
487	58.0	B37	C
488	30.0	NaN	S
489	9.0	NaN	S
490	NaN	NaN	S
491	21.0	NaN	S
492	55.0	C30	S
493	71.0	NaN	C
494	21.0	NaN	S
495	NaN	NaN	C
496	54.0	D20	C
497	NaN	NaN	S
498	25.0	C22 C26	S
499	24.0	NaN	S
500	17.0	NaN	S
501	21.0	NaN	Q
502	NaN	NaN	Q
503	37.0	NaN	S
504	16.0	B79	S
505	18.0	C65	C
506	33.0	NaN	S
507	NaN	NaN	S
508	28.0	NaN	S
509	26.0	NaN	S
510	29.0	NaN	Q
511	NaN	NaN	S
512	36.0	E25	S
513	54.0	NaN	C
514	24.0	NaN	S
515	47.0	D46	S
516	34.0	F33	S
517	NaN	NaN	Q
518	36.0	NaN	S
519	32.0	NaN	S
520	30.0	B73	S
521	22.0	NaN	S
522	NaN	NaN	C
523	44.0	B18	C
524	NaN	NaN	C
525	40.5	NaN	Q
526	50.0	NaN	S
527	NaN	C95	S
528	39.0	NaN	S
529	23.0	NaN	S
530	2.0	NaN	S
531	NaN	NaN	C
532	17.0	NaN	C
533	NaN	NaN	C
534	30.0	NaN	S
535	7.0	NaN	S
536	45.0	B38	S
537	30.0	NaN	C
538	NaN	NaN	S
539	22.0	B39	C
540	36.0	B22	S
541	9.0	NaN	S
542	11.0	NaN	S
543	32.0	NaN	S
544	50.0	C86	C
545	64.0	NaN	S
546	19.0	NaN	S
547	NaN	NaN	C
548	33.0	NaN	S
549	8.0	NaN	S
550	17.0	C70	C
551	27.0	NaN	S
552	NaN	NaN	Q
553	22.0	NaN	C
554	22.0	NaN	S
555	62.0	NaN	S
556	48.0	A16	C
557	NaN	NaN	C
558	39.0	E67	S
559	36.0	NaN	S
560	NaN	NaN	Q
561	40.0	NaN	S
562	28.0	NaN	S
563	NaN	NaN	S
564	NaN	NaN	S
565	24.0	NaN	S
566	19.0	NaN	S
567	29.0	NaN	S
568	NaN	NaN	C
569	32.0	NaN	S
570	62.0	NaN	S
571	53.0	C101	S
572	36.0	E25	S
573	NaN	NaN	Q
574	16.0	NaN	S
575	19.0	NaN	S
576	34.0	NaN	S
577	39.0	E44	S
578	NaN	NaN	C
579	32.0	NaN	S
580	25.0	NaN	S
581	39.0	C68	C
582	54.0	NaN	S
583	36.0	A10	C
584	NaN	NaN	C
585	18.0	E68	S
586	47.0	NaN	S
587	60.0	B41	C
588	22.0	NaN	S
589	NaN	NaN	S
590	35.0	NaN	S
591	52.0	D20	C
592	47.0	NaN	S
593	NaN	NaN	Q
594	37.0	NaN	S
595	36.0	NaN	S
596	NaN	NaN	S
597	49.0	NaN	S
598	NaN	NaN	C
599	49.0	A20	C
600	24.0	NaN	S
601	NaN	NaN	S
602	NaN	NaN	S
603	44.0	NaN	S
604	35.0	NaN	C
605	36.0	NaN	S
606	30.0	NaN	S
607	27.0	NaN	S
608	22.0	NaN	C
609	40.0	C125	S
610	39.0	NaN	S
611	NaN	NaN	S
612	NaN	NaN	Q
613	NaN	NaN	Q
614	35.0	NaN	S
615	24.0	NaN	S
616	34.0	NaN	S
617	26.0	NaN	S
618	4.0	F4	S
619	26.0	NaN	S
620	27.0	NaN	C
621	42.0	D19	S
622	20.0	NaN	C
623	21.0	NaN	S
624	21.0	NaN	S
625	61.0	D50	S
626	57.0	NaN	Q
627	21.0	D9	S
628	26.0	NaN	S
629	NaN	NaN	Q
630	80.0	A23	S
631	51.0	NaN	S
632	32.0	B50	C
633	NaN	NaN	S
634	9.0	NaN	S
635	28.0	NaN	S
636	32.0	NaN	S
637	31.0	NaN	S
638	41.0	NaN	S
639	NaN	NaN	S
640	20.0	NaN	S
641	24.0	B35	C
642	2.0	NaN	S
643	NaN	NaN	S
644	0.75	NaN	C
645	48.0	D33	C
646	19.0	NaN	S
647	56.0	A26	C
648	NaN	NaN	S
649	23.0	NaN	S
650	NaN	NaN	S
651	18.0	NaN	S
652	21.0	NaN	S
653	NaN	NaN	Q
654	18.0	NaN	Q
655	24.0	NaN	S
656	NaN	NaN	S
657	32.0	NaN	Q
658	23.0	NaN	S
659	58.0	D48	C
660	50.0	NaN	S
661	40.0	NaN	C
662	47.0	E58	S
663	36.0	NaN	S
664	20.0	NaN	S
665	32.0	NaN	S
666	25.0	NaN	S
667	NaN	NaN	S
668	43.0	NaN	S
669	NaN	C126	S
670	40.0	NaN	S
671	31.0	B71	S
672	70.0	NaN	S
673	31.0	NaN	S
674	NaN	NaN	S
675	18.0	NaN	S
676	24.5	NaN	S
677	18.0	NaN	S
678	43.0	NaN	S
679	36.0	B51 B53 B55	C
680	NaN	NaN	Q
681	27.0	D49	C
682	20.0	NaN	S
683	14.0	NaN	S
684	60.0	NaN	S
685	25.0	NaN	C
686	14.0	NaN	S
687	19.0	NaN	S
688	18.0	NaN	S
689	15.0	B5	S
690	31.0	B20	S
691	4.0	NaN	C
692	NaN	NaN	S
693	25.0	NaN	C
694	60.0	NaN	S
695	52.0	NaN	S
696	44.0	NaN	S
697	NaN	NaN	Q
698	49.0	C68	C
699	42.0	F G63	S
700	18.0	C62 C64	C
701	35.0	E24	S
702	18.0	NaN	C
703	25.0	NaN	Q
704	26.0	NaN	S
705	39.0	NaN	S
706	45.0	NaN	S
707	42.0	E24	S
708	22.0	NaN	S
709	NaN	NaN	C
710	24.0	C90	C
711	NaN	C124	S
712	48.0	C126	S
713	29.0	NaN	S
714	52.0	NaN	S
715	19.0	F G73	S
716	38.0	C45	C
717	27.0	E101	S
718	NaN	NaN	Q
719	33.0	NaN	S
720	6.0	NaN	S
721	17.0	NaN	S
722	34.0	NaN	S
723	50.0	NaN	S
724	27.0	E8	S
725	20.0	NaN	S
726	30.0	NaN	S
727	NaN	NaN	Q
728	25.0	NaN	S
729	25.0	NaN	S
730	29.0	B5	S
731	11.0	NaN	C
732	NaN	NaN	S
733	23.0	NaN	S
734	23.0	NaN	S
735	28.5	NaN	S
736	48.0	NaN	S
737	35.0	B101	C
738	NaN	NaN	S
739	NaN	NaN	S
740	NaN	D45	S
741	36.0	C46	S
742	21.0	B57 B59 B63 B66	C
743	24.0	NaN	S
744	31.0	NaN	S
745	70.0	B22	S
746	16.0	NaN	S
747	30.0	NaN	S
748	19.0	D30	S
749	31.0	NaN	Q
750	4.0	NaN	S
751	6.0	E121	S
752	33.0	NaN	S
753	23.0	NaN	S
754	48.0	NaN	S
755	0.67	NaN	S
756	28.0	NaN	S
757	18.0	NaN	S
758	34.0	NaN	S
759	33.0	B77	S
760	NaN	NaN	S
761	41.0	NaN	S
762	20.0	NaN	C
763	36.0	B96 B98	S
764	16.0	NaN	S
765	51.0	D11	S
766	NaN	NaN	C
767	30.5	NaN	Q
768	NaN	NaN	Q
769	32.0	NaN	S
770	24.0	NaN	S
771	48.0	NaN	S
772	57.0	E77	S
773	NaN	NaN	C
774	54.0	NaN	S
775	18.0	NaN	S
776	NaN	F38	Q
777	5.0	NaN	S
778	NaN	NaN	Q
779	43.0	B3	S
780	13.0	NaN	C
781	17.0	B20	S
782	29.0	D6	S
783	NaN	NaN	S
784	25.0	NaN	S
785	25.0	NaN	S
786	18.0	NaN	S
787	8.0	NaN	Q
788	1.0	NaN	S
789	46.0	B82 B84	C
790	NaN	NaN	Q
791	16.0	NaN	S
792	NaN	NaN	S
793	NaN	NaN	C
794	25.0	NaN	S
795	39.0	NaN	S
796	49.0	D17	S
797	31.0	NaN	S
798	30.0	NaN	C
799	30.0	NaN	S
800	34.0	NaN	S
801	31.0	NaN	S
802	11.0	B96 B98	S
803	0.42	NaN	C
804	27.0	NaN	S
805	31.0	NaN	S
806	39.0	A36	S
807	18.0	NaN	S
808	39.0	NaN	S
809	33.0	E8	S
810	26.0	NaN	S
811	39.0	NaN	S
812	35.0	NaN	S
813	6.0	NaN	S
814	30.5	NaN	S
815	NaN	B102	S
816	23.0	NaN	S
817	31.0	NaN	C
818	43.0	NaN	S
819	10.0	NaN	S
820	52.0	B69	S
821	27.0	NaN	S
822	38.0	NaN	S
823	27.0	E121	S
824	2.0	NaN	S
825	NaN	NaN	Q
826	NaN	NaN	S
827	1.0	NaN	C
828	NaN	NaN	Q
829	62.0	B28	NaN
830	15.0	NaN	C
831	0.83	NaN	S
832	NaN	NaN	C
833	23.0	NaN	S
834	18.0	NaN	S
835	39.0	E49	C
836	21.0	NaN	S
837	NaN	NaN	S
838	32.0	NaN	S
839	NaN	C47	C
840	20.0	NaN	S
841	16.0	NaN	S
842	30.0	NaN	C
843	34.5	NaN	C
844	17.0	NaN	S
845	42.0	NaN	S
846	NaN	NaN	S
847	35.0	NaN	C
848	28.0	NaN	S
849	NaN	C92	C
850	4.0	NaN	S
851	74.0	NaN	S
852	9.0	NaN	C
853	16.0	D28	S
854	44.0	NaN	S
855	18.0	NaN	S
856	45.0	NaN	S
857	51.0	E17	S
858	24.0	NaN	C
859	NaN	NaN	C
860	41.0	NaN	S
861	21.0	NaN	S
862	48.0	D17	S
863	NaN	NaN	S
864	24.0	NaN	S
865	42.0	NaN	S
866	27.0	NaN	C
867	31.0	A24	S
868	NaN	NaN	S
869	4.0	NaN	S
870	26.0	NaN	S
871	47.0	D35	S
872	33.0	B51 B53 B55	S
873	47.0	NaN	S
874	28.0	NaN	C
875	15.0	NaN	C
876	20.0	NaN	S
877	19.0	NaN	S
878	NaN	NaN	S
879	56.0	C50	C
880	25.0	NaN	S
881	33.0	NaN	S
882	22.0	NaN	S
883	28.0	NaN	S
884	25.0	NaN	S
885	39.0	NaN	Q
886	27.0	NaN	S
887	19.0	B42	S
888	NaN	NaN	S
889	26.0	C148	C
890	32.0	NaN	Q

891 rows × 3 columns

Removing missing values

The dropna() function allows dropping Rows/Columns with missing NaN values. The axis=0 means that we delete all rows in which cells contain NaN. Since we use inplace=True, the dataframe will be modified after removing the rows.

# Removing all rows with NaN
titanic_df.dropna(axis=0, how='any', inplace=True)

# The size of the resulting dataset
titanic_df.shape

(183, 12)

Accessing Data

The bracket-based indexing operator

The bracket-based indexing operator helps us to extract specific columns from a dataframe.

# Get the only columns "Name", "Sex", "Age" of the first five rows
titanic_df[["Name", "Sex", "Age"]][:4]

index	Name	Sex	Age
1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	NaN	38.0
3	Futrelle, Mrs. Jacques Heath (Lily May Peel)	NaN	35.0
6	McCarthy, Mr. Timothy J	NaN	54.0
10	Sandstrom, Miss. Marguerite Rut	NaN	4.0

Accessing data by the key

We use the iloc() function to access the first five rows in our dataframe.

titanic_df.iloc[:5]

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_squared
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	NaN	38.0	1	0	PC 17599	71.2833	C85	C	1444.0
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	NaN	35.0	1	0	113803	53.1	C123	S	1225.0
6	7	0	1	McCarthy, Mr. Timothy J	NaN	54.0	0	0	17463	51.8625	E46	S	2916.0
10	11	1	3	Sandstrom, Miss. Marguerite Rut	NaN	4.0	1	1	PP 9549	16.7	G6	S	16.0
11	12	1	1	Bonnell, Miss. Elizabeth	NaN	58.0	0	0	113783	26.55	C103	S	3364.0

Integer-location-based indexing

To find out the rows of the most senior passenger or a passenger who paid the highest fare in the dataset, we can use the integer-location-based indexing and the argmax() function.

# The most senior passenger
titanic_df.iloc[[titanic_df['Age'].argmax()]]

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_squared
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	NaN	80.0	0	0	27042	30.0	A23	S	6400.0

# Paid the highest fair
titanic_df.iloc[[titanic_df['Fare'].argmax()]]

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_squared
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	NaN	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	1296.0

Working with columns

To access a specific column, you can use the square brackets notation:

titanic_df['Age']

1      38.0
3      35.0
6      54.0
10      4.0
11     58.0
       ... 
871    47.0
872    33.0
879    56.0
887    19.0
889    26.0
Name: Age, Length: 183, dtype: float64

This will return the ‘Age’ column as a Pandas Series.

To select multiple columns, you can pass a list of column names:

titanic_df[['Age', 'Fare']]

index	Age	Fare
1	38.0	71.2833
3	35.0	53.1
6	54.0	51.8625
10	4.0	16.7
11	58.0	26.55
21	34.0	13.0
23	28.0	35.5
27	19.0	263.0
52	49.0	76.7292
54	65.0	61.9792
62	45.0	83.475
66	29.0	10.5
75	25.0	7.65
88	23.0	263.0
92	46.0	61.175
96	71.0	34.6542
97	23.0	63.3583
102	21.0	77.2875
110	47.0	52.0
118	24.0	247.5208
123	32.5	13.0
124	54.0	77.2875
136	19.0	26.2833
137	37.0	53.1
139	24.0	79.2
148	36.5	26.0
151	22.0	66.6
170	61.0	33.5
174	56.0	30.6958
177	50.0	28.7125
183	1.0	39.0
193	3.0	26.0
194	44.0	27.7208
195	58.0	146.5208
205	2.0	10.4625
209	40.0	31.0
215	31.0	113.275
218	32.0	76.2917
224	38.0	90.0
230	35.0	83.475
245	44.0	90.0
248	37.0	52.5542
251	29.0	10.4625
252	62.0	26.55
257	30.0	86.5
262	52.0	79.65
263	40.0	0.0
268	58.0	153.4625
269	35.0	135.6333
273	37.0	29.7
275	63.0	77.9583
291	19.0	91.0792
292	36.0	12.875
297	2.0	151.55
299	50.0	247.5208
305	0.92	151.55
307	17.0	108.9
309	30.0	56.9292
310	24.0	83.1583
311	18.0	262.375
318	31.0	164.8667
319	40.0	134.5
325	36.0	135.6333
327	36.0	13.0
329	16.0	57.9792
331	45.5	28.5
332	38.0	153.4625
336	29.0	66.6
337	41.0	134.5
339	45.0	35.5
340	2.0	26.0
341	24.0	263.0
345	24.0	13.0
356	22.0	55.0
366	60.0	75.25
369	24.0	69.3
370	25.0	55.4417
377	27.0	211.5
390	36.0	120.0
393	23.0	113.275
394	24.0	16.7
412	33.0	90.0
429	32.0	8.05
430	28.0	26.55
434	50.0	55.9
435	14.0	120.0
438	64.0	263.0
445	4.0	81.8583
449	52.0	30.5
452	30.0	27.75
453	49.0	89.1042
456	65.0	26.55
460	48.0	26.55
462	47.0	38.5
473	23.0	13.7917
484	25.0	91.0792
486	35.0	90.0
487	58.0	29.7
492	55.0	30.5
496	54.0	78.2667
498	25.0	151.55
504	16.0	86.5
505	18.0	108.9
512	36.0	26.2875
515	47.0	34.0208
516	34.0	10.5
520	30.0	93.5
523	44.0	57.9792
536	45.0	26.55
539	22.0	49.5
540	36.0	71.0
544	50.0	106.425
550	17.0	110.8833
556	48.0	39.6
558	39.0	79.65
571	53.0	51.4792
572	36.0	26.3875
577	39.0	55.9
581	39.0	110.8833
583	36.0	40.125
585	18.0	79.65
587	60.0	79.2
591	52.0	78.2667
599	49.0	56.9292
609	40.0	153.4625
618	4.0	39.0
621	42.0	52.5542
625	61.0	32.3208
627	21.0	77.9583
630	80.0	30.0
632	32.0	30.5
641	24.0	69.3
645	48.0	76.7292
647	56.0	35.5
659	58.0	113.275
662	47.0	25.5875
671	31.0	52.0
679	36.0	512.3292
681	27.0	76.7292
689	15.0	211.3375
690	31.0	57.0
698	49.0	110.8833
699	42.0	7.65
700	18.0	227.525
701	35.0	26.2875
707	42.0	26.2875
710	24.0	49.5042
712	48.0	52.0
715	19.0	7.65
716	38.0	227.525
717	27.0	10.5
724	27.0	53.1
730	29.0	211.3375
737	35.0	512.3292
741	36.0	78.85
742	21.0	262.375
745	70.0	71.0
748	19.0	53.1
751	6.0	12.475
759	33.0	86.5
763	36.0	120.0
765	51.0	77.9583
772	57.0	10.5
779	43.0	211.3375
781	17.0	57.0
782	29.0	30.0
789	46.0	79.2
796	49.0	25.9292
802	11.0	120.0
806	39.0	0.0
809	33.0	53.1
820	52.0	93.5
823	27.0	12.475
835	39.0	83.1583
853	16.0	39.4
857	51.0	26.55
862	48.0	25.9292
867	31.0	50.4958
871	47.0	52.5542
872	33.0	5.0
879	56.0	83.1583
887	19.0	30.0
889	26.0	30.0

This will return a new DataFrame with only the ‘Age’ and ‘Fare’ columns.

Conditional filtering

The filter function selects rows from a DataFrame based on a boolean condition. In the example above, the filter function selects only the rows where the ‘Age’ column is not null (i.e., where the value is not missing).

# Use the filter function to select only the rows where 'Age' is not null
filtered_df = titanic_df[titanic_df['Age'].notnull()]

Boolean indexing

You can also use boolean indexing to filter rows based on a condition:

titanic_df[titanic_df['Age'] > 30]

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1	C123	S
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.55	C103	S
21	22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0	D56	S
52	53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.0	1	0	PC 17572	76.7292	D33	C
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
62	63	0	1	Harris, Mr. Henry Birkhardt	male	45.0	1	0	36973	83.475	C83	S
92	93	0	1	Chaffee, Mr. Herbert Fuller	male	46.0	1	0	W.E.P. 5734	61.175	E31	S
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
110	111	0	1	Porter, Mr. Walter Chamberlain	male	47.0	0	0	110465	52.0	C110	S
123	124	1	2	Webber, Miss. Susan	female	32.5	0	0	27267	13.0	E101	S
124	125	0	1	White, Mr. Percival Wayland	male	54.0	0	1	35281	77.2875	D26	S
137	138	0	1	Futrelle, Mr. Jacques Heath	male	37.0	1	0	113803	53.1	C123	S
148	149	0	2	Navratil, Mr. Michel (“Louis M Hoffman”)	male	36.5	0	2	230080	26.0	F2	S
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0	0	0	111240	33.5	B19	S
174	175	0	1	Smith, Mr. James Clinch	male	56.0	0	0	17764	30.6958	A7	C
177	178	0	1	Isham, Miss. Ann Elizabeth	female	50.0	0	0	PC 17595	28.7125	C49	C
194	195	1	1	Brown, Mrs. James Joseph (Margaret Tobin)	female	44.0	0	0	PC 17610	27.7208	B4	C
195	196	1	1	Lurette, Miss. Elise	female	58.0	0	0	PC 17569	146.5208	B80	C
209	210	1	1	Blank, Mr. Henry	male	40.0	0	0	112277	31.0	A31	C
215	216	1	1	Newell, Miss. Madeleine	female	31.0	1	0	35273	113.275	D36	C
218	219	1	1	Bazzani, Miss. Albina	female	32.0	0	0	11813	76.2917	D15	C
224	225	1	1	Hoyt, Mr. Frederick Maxfield	male	38.0	1	0	19943	90.0	C93	S
230	231	1	1	Harris, Mrs. Henry Birkhardt (Irene Wallach)	female	35.0	1	0	36973	83.475	C83	S
245	246	0	1	Minahan, Dr. William Edward	male	44.0	2	0	19928	90.0	C78	Q
248	249	1	1	Beckwith, Mr. Richard Leonard	male	37.0	1	1	11751	52.5542	D35	S
252	253	0	1	Stead, Mr. William Thomas	male	62.0	0	0	113514	26.55	C87	S
262	263	0	1	Taussig, Mr. Emil	male	52.0	1	1	110413	79.65	E67	S
263	264	0	1	Harrison, Mr. William	male	40.0	0	0	112059	0.0	B94	S
268	269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.0	0	1	PC 17582	153.4625	C125	S
269	270	1	1	Bissette, Miss. Amelia	female	35.0	0	0	PC 17760	135.6333	C99	S
273	274	0	1	Natsch, Mr. Charles H	male	37.0	0	1	PC 17596	29.7	C118	C
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
292	293	0	2	Levy, Mr. Rene Jacques	male	36.0	0	0	SC/Paris 2163	12.875	D	C
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
318	319	1	1	Wick, Miss. Mary Natalie	female	31.0	0	2	36928	164.8667	C7	S
319	320	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)	female	40.0	1	1	16966	134.5	E34	C
325	326	1	1	Young, Miss. Marie Grice	female	36.0	0	0	PC 17760	135.6333	C32	C
327	328	1	2	Ball, Mrs. (Ada E Hall)	female	36.0	0	0	28551	13.0	D	S
331	332	0	1	Partner, Mr. Austen	male	45.5	0	0	113043	28.5	C124	S
332	333	0	1	Graham, Mr. George Edward	male	38.0	0	1	PC 17582	153.4625	C91	S
337	338	1	1	Burns, Miss. Elizabeth Margaret	female	41.0	0	0	16966	134.5	E40	C
339	340	0	1	Blackwell, Mr. Stephen Weart	male	45.0	0	0	113784	35.5	T	S
366	367	1	1	Warren, Mrs. Frank Manley (Anna Sophia Atkinson)	female	60.0	1	0	110813	75.25	D37	C
390	391	1	1	Carter, Mr. William Ernest	male	36.0	1	2	113760	120.0	B96 B98	S
412	413	1	1	Minahan, Miss. Daisy E	female	33.0	1	0	19928	90.0	C78	Q
429	430	1	3	Pickard, Mr. Berk (Berk Trembisky)	male	32.0	0	0	SOTON/O.Q. 392078	8.05	E10	S
434	435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9	E44	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0	C23 C25 C27	S
449	450	1	1	Peuchen, Major. Arthur Godfrey	male	52.0	0	0	113786	30.5	C104	S
453	454	1	1	Goldenberg, Mr. Samuel L	male	49.0	1	0	17453	89.1042	C92	C
456	457	0	1	Millet, Mr. Francis Davis	male	65.0	0	0	13509	26.55	E38	S
460	461	1	1	Anderson, Mr. Harry	male	48.0	0	0	19952	26.55	E12	S
462	463	0	1	Gee, Mr. Arthur H	male	47.0	0	0	111320	38.5	E63	S
486	487	1	1	Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)	female	35.0	1	0	19943	90.0	C93	S
487	488	0	1	Kent, Mr. Edward Austin	male	58.0	0	0	11771	29.7	B37	C
492	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5	C30	S
496	497	1	1	Eustis, Miss. Elizabeth Mussey	female	54.0	1	0	36947	78.2667	D20	C
512	513	1	1	McGough, Mr. James Robert	male	36.0	0	0	PC 17473	26.2875	E25	S
515	516	0	1	Walker, Mr. William Anderson	male	47.0	0	0	36967	34.0208	D46	S
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5	F33	S
523	524	1	1	Hippach, Mrs. Louis Albert (Ida Sophia Fischer)	female	44.0	0	1	111361	57.9792	B18	C
536	537	0	1	Butt, Major. Archibald Willingham	male	45.0	0	0	113050	26.55	B38	S
540	541	1	1	Crosby, Miss. Harriet R	female	36.0	0	2	WE/P 5735	71.0	B22	S
544	545	0	1	Douglas, Mr. Walter Donald	male	50.0	1	0	PC 17761	106.425	C86	C
556	557	1	1	Duff Gordon, Lady. (Lucille Christiana Sutherland) (“Mrs Morgan”)	female	48.0	1	0	11755	39.6	A16	C
558	559	1	1	Taussig, Mrs. Emil (Tillie Mandelbaum)	female	39.0	1	1	110413	79.65	E67	S
571	572	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53.0	2	0	11769	51.4792	C101	S
572	573	1	1	Flynn, Mr. John Irwin (“Irving”)	male	36.0	0	0	PC 17474	26.3875	E25	S
577	578	1	1	Silvey, Mrs. William Baird (Alice Munger)	female	39.0	1	0	13507	55.9	E44	S
581	582	1	1	Thayer, Mrs. John Borland (Marian Longstreth Morris)	female	39.0	1	1	17421	110.8833	C68	C
583	584	0	1	Ross, Mr. John Hugo	male	36.0	0	0	13049	40.125	A10	C
587	588	1	1	Frolicher-Stehli, Mr. Maxmillian	male	60.0	1	1	13567	79.2	B41	C
591	592	1	1	Stephenson, Mrs. Walter Bertram (Martha Eustis)	female	52.0	1	0	36947	78.2667	D20	C
599	600	1	1	Duff Gordon, Sir. Cosmo Edmund (“Mr Morgan”)	male	49.0	1	0	PC 17485	56.9292	A20	C
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
621	622	1	1	Kimball, Mr. Edwin Nelson Jr	male	42.0	1	0	11753	52.5542	D19	S
625	626	0	1	Sutton, Mr. Frederick	male	61.0	0	0	36963	32.3208	D50	S
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0	A23	S
632	633	1	1	Stahelin-Maeglin, Dr. Max	male	32.0	0	0	13214	30.5	B50	C
645	646	1	1	Harper, Mr. Henry Sleeper	male	48.0	1	0	PC 17572	76.7292	D33	C
647	648	1	1	Simonius-Blumer, Col. Oberst Alfons	male	56.0	0	0	13213	35.5	A26	C
659	660	0	1	Newell, Mr. Arthur Webster	male	58.0	0	2	35273	113.275	D48	C
662	663	0	1	Colley, Mr. Edward Pomeroy	male	47.0	0	0	5727	25.5875	E58	S
671	672	0	1	Davidson, Mr. Thornton	male	31.0	1	0	F.C. 12750	52.0	B71	S
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
690	691	1	1	Dick, Mr. Albert Adrian	male	31.0	1	0	17474	57.0	B20	S
698	699	0	1	Thayer, Mr. John Borland	male	49.0	1	1	17421	110.8833	C68	C
699	700	0	3	Humblen, Mr. Adolf Mathias Nicolai Olsen	male	42.0	0	0	348121	7.65	F G63	S
701	702	1	1	Silverthorne, Mr. Spencer Victor	male	35.0	0	0	PC 17475	26.2875	E24	S
707	708	1	1	Calderhead, Mr. Edward Pennington	male	42.0	0	0	PC 17476	26.2875	E24	S
712	713	1	1	Taylor, Mr. Elmer Zebley	male	48.0	1	0	19996	52.0	C126	S
716	717	1	1	Endres, Miss. Caroline Louise	female	38.0	0	0	PC 17757	227.525	C45	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
741	742	0	1	Cavendish, Mr. Tyrell William	male	36.0	1	0	19877	78.85	C46	S
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0	B22	S
759	760	1	1	Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)	female	33.0	0	0	110152	86.5	B77	S
763	764	1	1	Carter, Mrs. William Ernest (Lucile Polk)	female	36.0	1	2	113760	120.0	B96 B98	S
765	766	1	1	Hogeboom, Mrs. John C (Anna Andrews)	female	51.0	1	0	13502	77.9583	D11	S
772	773	0	2	Mack, Mrs. (Mary)	female	57.0	0	0	S.O./P.P. 3	10.5	E77	S
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)	female	43.0	0	1	24160	211.3375	B3	S
789	790	0	1	Guggenheim, Mr. Benjamin	male	46.0	0	0	PC 17593	79.2	B82 B84	C
796	797	1	1	Leader, Dr. Alice (Farnham)	female	49.0	0	0	17465	25.9292	D17	S
806	807	0	1	Andrews, Mr. Thomas Jr	male	39.0	0	0	112050	0.0	A36	S
809	810	1	1	Chambers, Mrs. Norman Campbell (Bertha Griggs)	female	33.0	1	0	113806	53.1	E8	S
820	821	1	1	Hays, Mrs. Charles Melville (Clara Jennings Gregg)	female	52.0	1	1	12749	93.5	B69	S
835	836	1	1	Compton, Miss. Sara Rebecca	female	39.0	1	1	PC 17756	83.1583	E49	C
857	858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.55	E17	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Barron)	female	48.0	0	0	17466	25.9292	D17	S
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0	B51 B53 B55	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C

This will return a new DataFrame with only the rows where the ‘Age’ column is greater than 30.

Modifying the data

Changing columns

To create a new column, you can use the dot notation:

titanic_df['Age_squared'] = titanic_df['Age'] ** 2

This will add a new column to the DataFrame called ‘Age_squared,’ the square of the ‘Age’ column.

titanic_df['Age_squared']

1      1444.0
3      1225.0
6      2916.0
10       16.0
11     3364.0
        ...  
871    2209.0
872    1089.0
879    3136.0
887     361.0
889     676.0
Name: Age_squared, Length: 183, dtype: float64

Dropping columns

To drop a column, you can use the drop method:

titanic_df.drop('Age_squared', axis=1)

This will return a new DataFrame with the ‘Age_squared’ column removed.

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1	C123	S
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7	G6	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.55	C103	S
21	22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0	D56	S
23	24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5	A6	S
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0	C23 C25 C27	S
52	53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.0	1	0	PC 17572	76.7292	D33	C
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
62	63	0	1	Harris, Mr. Henry Birkhardt	male	45.0	1	0	36973	83.475	C83	S
66	67	1	2	Nye, Mrs. (Elizabeth Ramell)	female	29.0	0	0	C.A. 29395	10.5	F33	S
75	76	0	3	Moen, Mr. Sigurd Hansen	male	25.0	0	0	348123	7.65	F G73	S
88	89	1	1	Fortune, Miss. Mabel Helen	female	23.0	3	2	19950	263.0	C23 C25 C27	S
92	93	0	1	Chaffee, Mr. Herbert Fuller	male	46.0	1	0	W.E.P. 5734	61.175	E31	S
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
97	98	1	1	Greenfield, Mr. William Bertram	male	23.0	0	1	PC 17759	63.3583	D10 D12	C
102	103	0	1	White, Mr. Richard Frasar	male	21.0	0	1	35281	77.2875	D26	S
110	111	0	1	Porter, Mr. Walter Chamberlain	male	47.0	0	0	110465	52.0	C110	S
118	119	0	1	Baxter, Mr. Quigg Edmond	male	24.0	0	1	PC 17558	247.5208	B58 B60	C
123	124	1	2	Webber, Miss. Susan	female	32.5	0	0	27267	13.0	E101	S
124	125	0	1	White, Mr. Percival Wayland	male	54.0	0	1	35281	77.2875	D26	S
136	137	1	1	Newsom, Miss. Helen Monypeny	female	19.0	0	2	11752	26.2833	D47	S
137	138	0	1	Futrelle, Mr. Jacques Heath	male	37.0	1	0	113803	53.1	C123	S
139	140	0	1	Giglio, Mr. Victor	male	24.0	0	0	PC 17593	79.2	B86	C
148	149	0	2	Navratil, Mr. Michel (“Louis M Hoffman”)	male	36.5	0	2	230080	26.0	F2	S
151	152	1	1	Pears, Mrs. Thomas (Edith Wearne)	female	22.0	1	0	113776	66.6	C2	S
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0	0	0	111240	33.5	B19	S
174	175	0	1	Smith, Mr. James Clinch	male	56.0	0	0	17764	30.6958	A7	C
177	178	0	1	Isham, Miss. Ann Elizabeth	female	50.0	0	0	PC 17595	28.7125	C49	C
183	184	1	2	Becker, Master. Richard F	male	1.0	2	1	230136	39.0	F4	S
193	194	1	2	Navratil, Master. Michel M	male	3.0	1	1	230080	26.0	F2	S
194	195	1	1	Brown, Mrs. James Joseph (Margaret Tobin)	female	44.0	0	0	PC 17610	27.7208	B4	C
195	196	1	1	Lurette, Miss. Elise	female	58.0	0	0	PC 17569	146.5208	B80	C
205	206	0	3	Strom, Miss. Telma Matilda	female	2.0	0	1	347054	10.4625	G6	S
209	210	1	1	Blank, Mr. Henry	male	40.0	0	0	112277	31.0	A31	C
215	216	1	1	Newell, Miss. Madeleine	female	31.0	1	0	35273	113.275	D36	C
218	219	1	1	Bazzani, Miss. Albina	female	32.0	0	0	11813	76.2917	D15	C
224	225	1	1	Hoyt, Mr. Frederick Maxfield	male	38.0	1	0	19943	90.0	C93	S
230	231	1	1	Harris, Mrs. Henry Birkhardt (Irene Wallach)	female	35.0	1	0	36973	83.475	C83	S
245	246	0	1	Minahan, Dr. William Edward	male	44.0	2	0	19928	90.0	C78	Q
248	249	1	1	Beckwith, Mr. Richard Leonard	male	37.0	1	1	11751	52.5542	D35	S
251	252	0	3	Strom, Mrs. Wilhelm (Elna Matilda Persson)	female	29.0	1	1	347054	10.4625	G6	S
252	253	0	1	Stead, Mr. William Thomas	male	62.0	0	0	113514	26.55	C87	S
257	258	1	1	Cherry, Miss. Gladys	female	30.0	0	0	110152	86.5	B77	S
262	263	0	1	Taussig, Mr. Emil	male	52.0	1	1	110413	79.65	E67	S
263	264	0	1	Harrison, Mr. William	male	40.0	0	0	112059	0.0	B94	S
268	269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.0	0	1	PC 17582	153.4625	C125	S
269	270	1	1	Bissette, Miss. Amelia	female	35.0	0	0	PC 17760	135.6333	C99	S
273	274	0	1	Natsch, Mr. Charles H	male	37.0	0	1	PC 17596	29.7	C118	C
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
291	292	1	1	Bishop, Mrs. Dickinson H (Helen Walton)	female	19.0	1	0	11967	91.0792	B49	C
292	293	0	2	Levy, Mr. Rene Jacques	male	36.0	0	0	SC/Paris 2163	12.875	D	C
297	298	0	1	Allison, Miss. Helen Loraine	female	2.0	1	2	113781	151.55	C22 C26	S
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.55	C22 C26	S
307	308	1	1	Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)	female	17.0	1	0	PC 17758	108.9	C65	C
309	310	1	1	Francatelli, Miss. Laura Mabel	female	30.0	0	0	PC 17485	56.9292	E36	C
310	311	1	1	Hays, Miss. Margaret Bechstein	female	24.0	0	0	11767	83.1583	C54	C
311	312	1	1	Ryerson, Miss. Emily Borie	female	18.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C
318	319	1	1	Wick, Miss. Mary Natalie	female	31.0	0	2	36928	164.8667	C7	S
319	320	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)	female	40.0	1	1	16966	134.5	E34	C
325	326	1	1	Young, Miss. Marie Grice	female	36.0	0	0	PC 17760	135.6333	C32	C
327	328	1	2	Ball, Mrs. (Ada E Hall)	female	36.0	0	0	28551	13.0	D	S
329	330	1	1	Hippach, Miss. Jean Gertrude	female	16.0	0	1	111361	57.9792	B18	C
331	332	0	1	Partner, Mr. Austen	male	45.5	0	0	113043	28.5	C124	S
332	333	0	1	Graham, Mr. George Edward	male	38.0	0	1	PC 17582	153.4625	C91	S
336	337	0	1	Pears, Mr. Thomas Clinton	male	29.0	1	0	113776	66.6	C2	S
337	338	1	1	Burns, Miss. Elizabeth Margaret	female	41.0	0	0	16966	134.5	E40	C
339	340	0	1	Blackwell, Mr. Stephen Weart	male	45.0	0	0	113784	35.5	T	S
340	341	1	2	Navratil, Master. Edmond Roger	male	2.0	1	1	230080	26.0	F2	S
341	342	1	1	Fortune, Miss. Alice Elizabeth	female	24.0	3	2	19950	263.0	C23 C25 C27	S
345	346	1	2	Brown, Miss. Amelia “Mildred”	female	24.0	0	0	248733	13.0	F33	S
356	357	1	1	Bowerman, Miss. Elsie Edith	female	22.0	0	1	113505	55.0	E33	S
366	367	1	1	Warren, Mrs. Frank Manley (Anna Sophia Atkinson)	female	60.0	1	0	110813	75.25	D37	C
369	370	1	1	Aubart, Mme. Leontine Pauline	female	24.0	0	0	PC 17477	69.3	B35	C
370	371	1	1	Harder, Mr. George Achilles	male	25.0	1	0	11765	55.4417	E50	C
377	378	0	1	Widener, Mr. Harry Elkins	male	27.0	0	2	113503	211.5	C82	C
390	391	1	1	Carter, Mr. William Ernest	male	36.0	1	2	113760	120.0	B96 B98	S
393	394	1	1	Newell, Miss. Marjorie	female	23.0	1	0	35273	113.275	D36	C
394	395	1	3	Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)	female	24.0	0	2	PP 9549	16.7	G6	S
412	413	1	1	Minahan, Miss. Daisy E	female	33.0	1	0	19928	90.0	C78	Q
429	430	1	3	Pickard, Mr. Berk (Berk Trembisky)	male	32.0	0	0	SOTON/O.Q. 392078	8.05	E10	S
430	431	1	1	Bjornstrom-Steffansson, Mr. Mauritz Hakan	male	28.0	0	0	110564	26.55	C52	S
434	435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9	E44	S
435	436	1	1	Carter, Miss. Lucile Polk	female	14.0	1	2	113760	120.0	B96 B98	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0	C23 C25 C27	S
445	446	1	1	Dodge, Master. Washington	male	4.0	0	2	33638	81.8583	A34	S
449	450	1	1	Peuchen, Major. Arthur Godfrey	male	52.0	0	0	113786	30.5	C104	S
452	453	0	1	Foreman, Mr. Benjamin Laventall	male	30.0	0	0	113051	27.75	C111	C
453	454	1	1	Goldenberg, Mr. Samuel L	male	49.0	1	0	17453	89.1042	C92	C
456	457	0	1	Millet, Mr. Francis Davis	male	65.0	0	0	13509	26.55	E38	S
460	461	1	1	Anderson, Mr. Harry	male	48.0	0	0	19952	26.55	E12	S
462	463	0	1	Gee, Mr. Arthur H	male	47.0	0	0	111320	38.5	E63	S
473	474	1	2	Jerwan, Mrs. Amin S (Marie Marthe Thuillard)	female	23.0	0	0	SC/AH Basle 541	13.7917	D	C
484	485	1	1	Bishop, Mr. Dickinson H	male	25.0	1	0	11967	91.0792	B49	C
486	487	1	1	Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)	female	35.0	1	0	19943	90.0	C93	S
487	488	0	1	Kent, Mr. Edward Austin	male	58.0	0	0	11771	29.7	B37	C
492	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5	C30	S
496	497	1	1	Eustis, Miss. Elizabeth Mussey	female	54.0	1	0	36947	78.2667	D20	C
498	499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0	1	2	113781	151.55	C22 C26	S
504	505	1	1	Maioni, Miss. Roberta	female	16.0	0	0	110152	86.5	B79	S
505	506	0	1	Penasco y Castellana, Mr. Victor de Satode	male	18.0	1	0	PC 17758	108.9	C65	C
512	513	1	1	McGough, Mr. James Robert	male	36.0	0	0	PC 17473	26.2875	E25	S
515	516	0	1	Walker, Mr. William Anderson	male	47.0	0	0	36967	34.0208	D46	S
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5	F33	S
520	521	1	1	Perreault, Miss. Anne	female	30.0	0	0	12749	93.5	B73	S
523	524	1	1	Hippach, Mrs. Louis Albert (Ida Sophia Fischer)	female	44.0	0	1	111361	57.9792	B18	C
536	537	0	1	Butt, Major. Archibald Willingham	male	45.0	0	0	113050	26.55	B38	S
539	540	1	1	Frolicher, Miss. Hedwig Margaritha	female	22.0	0	2	13568	49.5	B39	C
540	541	1	1	Crosby, Miss. Harriet R	female	36.0	0	2	WE/P 5735	71.0	B22	S
544	545	0	1	Douglas, Mr. Walter Donald	male	50.0	1	0	PC 17761	106.425	C86	C
550	551	1	1	Thayer, Mr. John Borland Jr	male	17.0	0	2	17421	110.8833	C70	C
556	557	1	1	Duff Gordon, Lady. (Lucille Christiana Sutherland) (“Mrs Morgan”)	female	48.0	1	0	11755	39.6	A16	C
558	559	1	1	Taussig, Mrs. Emil (Tillie Mandelbaum)	female	39.0	1	1	110413	79.65	E67	S
571	572	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53.0	2	0	11769	51.4792	C101	S
572	573	1	1	Flynn, Mr. John Irwin (“Irving”)	male	36.0	0	0	PC 17474	26.3875	E25	S
577	578	1	1	Silvey, Mrs. William Baird (Alice Munger)	female	39.0	1	0	13507	55.9	E44	S
581	582	1	1	Thayer, Mrs. John Borland (Marian Longstreth Morris)	female	39.0	1	1	17421	110.8833	C68	C
583	584	0	1	Ross, Mr. John Hugo	male	36.0	0	0	13049	40.125	A10	C
585	586	1	1	Taussig, Miss. Ruth	female	18.0	0	2	110413	79.65	E68	S
587	588	1	1	Frolicher-Stehli, Mr. Maxmillian	male	60.0	1	1	13567	79.2	B41	C
591	592	1	1	Stephenson, Mrs. Walter Bertram (Martha Eustis)	female	52.0	1	0	36947	78.2667	D20	C
599	600	1	1	Duff Gordon, Sir. Cosmo Edmund (“Mr Morgan”)	male	49.0	1	0	PC 17485	56.9292	A20	C
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
618	619	1	2	Becker, Miss. Marion Louise	female	4.0	2	1	230136	39.0	F4	S
621	622	1	1	Kimball, Mr. Edwin Nelson Jr	male	42.0	1	0	11753	52.5542	D19	S
625	626	0	1	Sutton, Mr. Frederick	male	61.0	0	0	36963	32.3208	D50	S
627	628	1	1	Longley, Miss. Gretchen Fiske	female	21.0	0	0	13502	77.9583	D9	S
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0	A23	S
632	633	1	1	Stahelin-Maeglin, Dr. Max	male	32.0	0	0	13214	30.5	B50	C
641	642	1	1	Sagesser, Mlle. Emma	female	24.0	0	0	PC 17477	69.3	B35	C
645	646	1	1	Harper, Mr. Henry Sleeper	male	48.0	1	0	PC 17572	76.7292	D33	C
647	648	1	1	Simonius-Blumer, Col. Oberst Alfons	male	56.0	0	0	13213	35.5	A26	C
659	660	0	1	Newell, Mr. Arthur Webster	male	58.0	0	2	35273	113.275	D48	C
662	663	0	1	Colley, Mr. Edward Pomeroy	male	47.0	0	0	5727	25.5875	E58	S
671	672	0	1	Davidson, Mr. Thornton	male	31.0	1	0	F.C. 12750	52.0	B71	S
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
681	682	1	1	Hassab, Mr. Hammad	male	27.0	0	0	PC 17572	76.7292	D49	C
689	690	1	1	Madill, Miss. Georgette Alexandra	female	15.0	0	1	24160	211.3375	B5	S
690	691	1	1	Dick, Mr. Albert Adrian	male	31.0	1	0	17474	57.0	B20	S
698	699	0	1	Thayer, Mr. John Borland	male	49.0	1	1	17421	110.8833	C68	C
699	700	0	3	Humblen, Mr. Adolf Mathias Nicolai Olsen	male	42.0	0	0	348121	7.65	F G63	S
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0	1	0	PC 17757	227.525	C62 C64	C
701	702	1	1	Silverthorne, Mr. Spencer Victor	male	35.0	0	0	PC 17475	26.2875	E24	S
707	708	1	1	Calderhead, Mr. Edward Pennington	male	42.0	0	0	PC 17476	26.2875	E24	S
710	711	1	1	Mayne, Mlle. Berthe Antonine (“Mrs de Villiers”)	female	24.0	0	0	PC 17482	49.5042	C90	C
712	713	1	1	Taylor, Mr. Elmer Zebley	male	48.0	1	0	19996	52.0	C126	S
715	716	0	3	Soholt, Mr. Peter Andreas Lauritz Andersen	male	19.0	0	0	348124	7.65	F G73	S
716	717	1	1	Endres, Miss. Caroline Louise	female	38.0	0	0	PC 17757	227.525	C45	C
717	718	1	2	Troutt, Miss. Edwina Celia “Winnie”	female	27.0	0	0	34218	10.5	E101	S
724	725	1	1	Chambers, Mr. Norman Campbell	male	27.0	1	0	113806	53.1	E8	S
730	731	1	1	Allen, Miss. Elisabeth Walton	female	29.0	0	0	24160	211.3375	B5	S
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
741	742	0	1	Cavendish, Mr. Tyrell William	male	36.0	1	0	19877	78.85	C46	S
742	743	1	1	Ryerson, Miss. Susan Parker “Suzette”	female	21.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0	B22	S
748	749	0	1	Marvin, Mr. Daniel Warner	male	19.0	1	0	113773	53.1	D30	S
751	752	1	3	Moor, Master. Meier	male	6.0	0	1	392096	12.475	E121	S
759	760	1	1	Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)	female	33.0	0	0	110152	86.5	B77	S
763	764	1	1	Carter, Mrs. William Ernest (Lucile Polk)	female	36.0	1	2	113760	120.0	B96 B98	S
765	766	1	1	Hogeboom, Mrs. John C (Anna Andrews)	female	51.0	1	0	13502	77.9583	D11	S
772	773	0	2	Mack, Mrs. (Mary)	female	57.0	0	0	S.O./P.P. 3	10.5	E77	S
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)	female	43.0	0	1	24160	211.3375	B3	S
781	782	1	1	Dick, Mrs. Albert Adrian (Vera Gillespie)	female	17.0	1	0	17474	57.0	B20	S
782	783	0	1	Long, Mr. Milton Clyde	male	29.0	0	0	113501	30.0	D6	S
789	790	0	1	Guggenheim, Mr. Benjamin	male	46.0	0	0	PC 17593	79.2	B82 B84	C
796	797	1	1	Leader, Dr. Alice (Farnham)	female	49.0	0	0	17465	25.9292	D17	S
802	803	1	1	Carter, Master. William Thornton II	male	11.0	1	2	113760	120.0	B96 B98	S
806	807	0	1	Andrews, Mr. Thomas Jr	male	39.0	0	0	112050	0.0	A36	S
809	810	1	1	Chambers, Mrs. Norman Campbell (Bertha Griggs)	female	33.0	1	0	113806	53.1	E8	S
820	821	1	1	Hays, Mrs. Charles Melville (Clara Jennings Gregg)	female	52.0	1	1	12749	93.5	B69	S
823	824	1	3	Moor, Mrs. (Beila)	female	27.0	0	1	392096	12.475	E121	S
835	836	1	1	Compton, Miss. Sara Rebecca	female	39.0	1	1	PC 17756	83.1583	E49	C
853	854	1	1	Lines, Miss. Mary Conover	female	16.0	0	1	PC 17592	39.4	D28	S
857	858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.55	E17	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Barron)	female	48.0	0	0	17466	25.9292	D17	S
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0	B51 B53 B55	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0	B42	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0	C148	C

183 rows × 12 columns

Data sorting

With sort_values() function, we sort values in ascending or descending order.

# Who paid too few
titanic_df.sort_values('Fare').head()

index	PassengerId	Pclass	Name	Sex	Age	Ticket	Fare	Cabin	Embarked	Age_squared
806	807	1	Andrews, Mr. Thomas Jr	NaN	39.0	112050	0.0	A36	S	1521.0
263	264	1	Harrison, Mr. William	NaN	40.0	112059	0.0	B94	S	1600.0
872	873	1	Carlsson, Mr. Frans Olof	NaN	33.0	695	5.0	B51 B53 B55	S	1089.0
715	716	3	Soholt, Mr. Peter Andreas Lauritz Andersen	NaN	19.0	348124	7.65	F G73	S	361.0
699	700	3	Humblen, Mr. Adolf Mathias Nicolai Olsen	NaN	42.0	348121	7.65	F G63	S	1764.0

# Who paid too much
titanic_df.sort_values('Fare', ascending=False).head()

index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_squared
737	738	1	1	Lesurer, Mr. Gustave J	NaN	35.0	0	0	PC 17755	512.3292	B101	C	1225.0
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	NaN	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	1296.0
88	89	1	1	Fortune, Miss. Mabel Helen	NaN	23.0	3	2	19950	263.0	C23 C25 C27	S	529.0
27	28	0	1	Fortune, Mr. Charles Alexander	NaN	19.0	3	2	19950	263.0	C23 C25 C27	S	361.0
438	439	0	1	Fortune, Mr. Mark	NaN	64.0	1	4	19950	263.0	C23 C25 C27	S	4096.0

Data Transformation

Pandas provides a wide range of data transformation functions, such as encoding, scaling, and normalization, which can be used to prepare the data for analysis.

For instance, the map function is used to apply a function or mapping to each element in a Pandas Series (i.e., a single column of a DataFrame). In the example above, the map function converts the strings in the ‘Sex’ column to numeric values (0 for ‘male’ and 1 for ‘female’).

# Use the map function to convert the 'Sex' column to a numeric value
titanic_df['Sex'] = titanic_df['Sex'].map({'male': 0, 'female': 1})

titanic_df['Sex'][:10]

1     1
3     1
6     0
10    1
11    1
21    0
23    0
27    0
52    1
54    0
Name: Sex, dtype: int64

Visualising data

The pyplot is a module in the matplotlib library in Python that provides functions for creating various plots and charts. It offers a simple, procedural interface to the underlying object-oriented plotting library in matplotlib. It provides functions for creating graphs, such as line plots, scatter plots, histograms, and bar charts, and for customizing the appearance of the plots, such as setting the axis labels, titles, and legends. It is often used with other data visualization libraries such as pandas and seaborn.

Bar plots

For instance, a Bar plot of the number of survivors by class can be created using the bar method of the plot function in Pandas. It can help to identify any differences in survival rates between the different classes.

Notice how the groupby function is used to group the data by class. The sum function calculates the number of survivors in each class. The plot.bar() method is then used to create the bar plot, and the title(), xlabel(), and ylabel() functions are used to add a title and labels to the plot. Finally, the show function is used to display the plot. To arrange the bars in a bar plot by their values, you can use the sort_values function before creating the plot.

import matplotlib.pyplot as plt

# Load the Titanic dataset
# titanic_df = pd.read_csv('titanic.csv')

# Group the data by class and calculate the number of survivors in each class
survival_by_class = titanic_df.groupby('Pclass')['Survived'].sum()

# Sort the values by the number of survivors
survival_by_class = survival_by_class.sort_values()

# Create a bar plot of the number of survivors by class
survival_by_class.plot.bar()

# Add a title and labels to the plot
plt.title('Number of survivors by class')
plt.xlabel('Class')
plt.ylabel('Number of survivors')

# Show the plot
plt.show()

This will create a bar plot showing the number of survivors by class, with the bars arranged in ascending order by the number of survivors.

Scatter plots

The scatter plot of age vs. fare plot can be created using the scatter method of the plot function in Pandas, and it can help identify any relationships between these two variables.

import pandas as pd
# Create a scatter plot of age vs fare
titanic_df.plot.scatter(x='Age', y='Fare')

# Add a title and labels to the plot
plt.title('Age vs Fare')
plt.xlabel('Age')
plt.ylabel('Fare')

# Show the plot
plt.show()

This will create a scatter plot showing the relationship between age and fare paid. The plot.scatter() method is used to create the scatter plot. Finally, the show() function is used to display the plot.

To color the dots in the scatter plot based on the values in a column, you will need to use a scatterplot function from the seaborn library rather than the scatter function from Pandas. Here is an example of how to do this:

import seaborn as sns
sns.scatterplot(x='Age', y='Fare', hue='Sex', data=titanic_df)

# Add a title and labels to the plot
plt.title('Age vs Fare')
plt.xlabel('Age')
plt.ylabel('Fare')

# Show the plot
plt.show()

This will create a scatter plot showing the relationship between age and fare paid, with the dots colored red for females and blue for males. The hue parameter is used to specify the column to use for the colors.

Histograms

The histogram of the age plot can be created using the hist method of the plot function in Pandas, and it can help visualize the age distribution among the passengers.

To create a histogram of the ages of the passengers using the Pandas library, you can use the hist method of the plot function. Here is an example of how to do this:

# Create a histogram of the ages of the passengers
titanic_df['Age'].plot.hist()

# Add a title and labels to the plot
plt.title('Age distribution of passengers')
plt.xlabel('Age')
plt.ylabel('Number of passengers')

# Show the plot
plt.show()

This will create a histogram showing the distribution of ages among the passengers. The plot.hist() method is used to create the histogram

Box plots

The box plot of fare by class can be created using the box method of the plot function in Pandas. It can help identify differences in fares paid between the different passenger classes.

To create a box plot of the fare paid by class using the Pandas library, you can use the box method of the plot function.

# Create a box plot of the fare by class
titanic_df.boxplot(column='Fare', by='Pclass')

# Add a title and labels to the plot
plt.title('Fare by class')
plt.xlabel('Class')
plt.ylabel('Fare')

# Show the plot
plt.show()

This will create a box plot showing the distribution of fare paid by class. The boxplot function is used to create the box plot.

Heatmaps

The heatmap of survival by class and gender plot can be created using the heatmap function from the seaborn library, and it can help identify any patterns in survival rates by class and gender.

Here is an example of how to create a heatmap using the seaborn library to visualize patterns in survival rates by class and gender. Notice that we did not encode the gender column in this code.

# Draw a heatmap function from the seaborn library to identify patterns in survival rates by class and gender.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the Titanic dataset
# titanic_df = pd.read_csv('titanic.csv')

# Pivot the data to create a survival rate by class and gender
pivoted = titanic_df.pivot_table(index='Pclass', columns='Sex', values='Survived', aggfunc='mean')

# Create a heatmap of the survival rate by class and gender
sns.heatmap(pivoted, cmap='Reds')

# Add a title and labels to the plot
plt.title('Survival rate by class and gender')
plt.xlabel('Gender')
plt.ylabel('Class')

# Show the plot
plt.show()

The survival rate by class and gender, heatmap

This will create a heatmap showing the survival rate by class and gender. The pivot_table function is used to pivot the data and create a survival rate by class and gender, and the heatmap function is used to generate the plot. The cmap parameter is used to specify the color map.

What about data analysis?

Excuse me? What is about the data analysis? The Pandas features above are mostly related to data exploration. Can you pinpoint the Python Pandas features related strictly to the data analysis and not to data exploration?

Yes, many of the features I listed earlier are related to data exploration, which is a step of the data analysis process. Here are some additional features of Pandas that are more specifically related to data analysis.

Statistical Analysis

Descriptive statistics

Pandas provides a wide range of statistical functions, such as mean, median, sum, and standard deviation, that can be applied to the data to gain insights into the underlying patterns and trends.

Besides using those above describe() function encompassing the statistical functions performed on the whole dataframe to calculate the mean, median, sum, standard deviation, min, and max of the fare column of the dataset:

# Calculate Mean
print(titanic_df['Fare'].mean())

# Calculate Median
print(titanic_df['Fare'].median())

# Calculate Sum
print(titanic_df['Fare'].sum())

# Calculate Standard Deviation
print(titanic_df['Fare'].std())

# Calculate the minimum value
print(titanic_df['Fare'].min())

# Calculate the maximum value
print(titanic_df['Fare'].max())

32.204207968574636
14.4542
28693.9493
49.6934285971809
0.0
512.3292

Correlation

Pandas provides functions to calculate correlation between columns of a DataFrame, which can be used to identify relationships between variables.

To calculate the correlation between fare and age columns:

correlation = titanic_df['Fare'].corr(titanic_df['Age'])
print(correlation)

0.09606669176903888

Moreover, other Python libraries, such as Scipy, provide various statistical tools to perform hypothesis testing, such as the Chi-squared test, t-test, and ANOVA, which can be used to make inferences about the data.

Data Aggregation

Pandas provides a wide range of data aggregation functions, such as groupby(), pivot_table(), and crosstab(), which can be used to summarize large datasets and extract insights from the data. These features are not exclusive to data exploration and are more related to data analysis itself and can assist in drawing inferences, making predictions, and discovering patterns from the data.

Before summarising or grouping data, it is helpful to see the list of columns in the dataframe.

titanic_df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

Data Grouping

Pandas’ groupby() function allows you to group data by one or more columns and then perform aggregate functions on the groups, such as sum, mean, and count, which is helpful for data analysis tasks. For instance, to group data by the class column and calculate the mean fare for each class:

grouped = titanic_df.groupby(by='Pclass')['Fare'].mean()
print(grouped)

Pclass
1    88.683228
2    18.444447
3    11.027500
Name: Fare, dtype: float64

Pivot Tables

Pivot tables in Pandas are similar to the groupby() functionality but allow more flexibility while returning a dataframe. Pivot tables can help summarise data with the function pd.pivot_table(). The parameter “data” takes in the input dataframe. When provided with “values,” it aggregates on the specified column, “index” and “columns” are to define a column or a list of columns to group the data, “aggfunc” is a function or list of functions to aggregate the data, “fill_value” is the value to fill in the missing values, the “margins” is when we want to add a row and a column for total values (the name is defined by the “margins_name” parameter), “dropna” is True when we want to exclude the NaN (missing values), “observed” is to show the categorical data groups, and “sort” True is for sorting the result.

For instance, we can summarise our titanic dataset to count the number of surviving passengers by their passenger class, and gender and calculate the mean fare for each passenger group.

pd.pivot_table(
    data=titanic_df,
    values="Fare", 
    index=["Pclass", "Sex"], 
    columns=["Survived"], 
    aggfunc=["count", "mean"], 
    fill_value=None, 
    margins=True, 
    dropna=True, 
    margins_name='All', 
    observed=False,
    sort=True
) 

Pclass	Sex	count, 0	count, 1	count, All	mean, 0	mean, 1	mean, All
1	female	3	91	94	110.60	105.98	106.13
1	male	77	45	122	62.89	74.64	67.23
2	female	6	70	76	18.25	22.29	21.97
2	male	91	17	108	19.49	21.10	19.74
3	female	72	72	144	19.77	12.46	16.12
3	male	300	47	347	12.20	15.58	12.66
All		549	342	891	22.12	48.40	32.20

Using Crosstab

The crosstab() function in Pandas is similar in its functionality to the pivot_table() and allows summarising data. Additionally, we can normalise the result values. For instance, we can get a normalised break down of the titanic dataset by the passenger class and their survival.

summarised_table = pd.crosstab(titanic_df['Survived'], titanic_df['Pclass'], normalize=True)
print(summarised_table)

Pclass      1    2    3
Survived               
0        0.09 0.11 0.42
1        0.15 0.10 0.13

Machine Learning

Machine learning is a subset of data analysis and is often considered a part of the data analysis process. Machine learning teaches computers to learn from data without being explicitly programmed. It involves using algorithms to analyze data, learn from it, and make predictions or decisions without human intervention. The Titanic dataset, while relatively small, contains a good amount of information, and it’s considered an excellent dataset to start learning machine learning. In my next blog post, I will go through the application of ML using the Titanic dataset. It will be simple and fun! Stay tuned.

Conclusion

Pandas is a library for data manipulation and analysis in Python. It provides data structures and functions for working with structured data, including data frames (similar to tables in a relational database) and series (similar to arrays). It is commonly used for data exploration, cleaning, and transformation. We have seen how to use Pandas to analyse and visualise the Titanic dataset. I hope this helps! Let me know if you have any questions.

In my next post, I will do more in-depth machine-learning experiments to predict the survival odds of Titanic passengers.

References

1. Pandas documentation

2. New Chat (chatGPT by OpenAI)

3. Code in my GitHub repository

About Elena

Elena, a PhD in Computer Science, simplifies AI concepts and helps you use machine learning.

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Citation

Elena Daehnhardt. (2023) 'Data exploration and analysis with Python Pandas', daehnhardt.com, 20 January 2023. Available at: https://daehnhardt.com/blog/2023/01/20/pandas-tutorial-with-titanic-dataset/

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