In [1]: import pandas as pd
This tutorial uses the Titanic data set, stored as CSV. The data consists of the following data columns:
PassengerId: Id of every passenger.
Survived: This feature have value 0 and 1. 0 for not survived and 1 for survived.
Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
Name: Name of passenger.
Sex: Gender of passenger.
Age: Age of passenger.
SibSp: Indication that passenger have siblings and spouse.
Parch: Whether a passenger is alone or have family.
Ticket: Ticket number of passenger.
Fare: Indicating the fare.
Cabin: The cabin of passenger.
Embarked: The embarked category.
I want to analyze the Titanic passenger data, available as a CSV file.
In [2]: titanic = pd.read_csv("data/titanic.csv")
pandas provides the read_csv() function to read data stored as a csv file into a pandas DataFrame. pandas supports many different file formats or data sources out of the box (csv, excel, sql, json, parquet, …), each of them with the prefix read_*.
read_csv()
DataFrame
read_*
Make sure to always have a check on the data after reading in the data. When displaying a DataFrame, the first and last 5 rows will be shown by default:
In [3]: titanic Out[3]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... 886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S 888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S 889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q [891 rows x 12 columns]
I want to see the first 8 rows of a pandas DataFrame.
In [4]: titanic.head(8) Out[4]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male ... 0 330877 8.4583 NaN Q 6 7 0 1 McCarthy, Mr. Timothy J male ... 0 17463 51.8625 E46 S 7 8 0 3 Palsson, Master. Gosta Leonard male ... 1 349909 21.0750 NaN S [8 rows x 12 columns]
To see the first N rows of a DataFrame, use the head() method with the required number of rows (in this case 8) as argument.
head()
Note
Interested in the last N rows instead? pandas also provides a tail() method. For example, titanic.tail(10) will return the last 10 rows of the DataFrame.
tail()
titanic.tail(10)
A check on how pandas interpreted each of the column data types can be done by requesting the pandas dtypes attribute:
dtypes
In [5]: titanic.dtypes Out[5]: PassengerId int64 Survived int64 Pclass int64 Name object Sex object Age float64 SibSp int64 Parch int64 Ticket object Fare float64 Cabin object Embarked object dtype: object
For each of the columns, the used data type is enlisted. The data types in this DataFrame are integers (int64), floats (float64) and strings (object).
int64
float64
object
When asking for the dtypes, no brackets are used! dtypes is an attribute of a DataFrame and Series. Attributes of DataFrame or Series do not need brackets. Attributes represent a characteristic of a DataFrame/Series, whereas a method (which requires brackets) do something with the DataFrame/Series as introduced in the first tutorial.
Series
My colleague requested the Titanic data as a spreadsheet.
In [6]: titanic.to_excel('titanic.xlsx', sheet_name='passengers', index=False)
Whereas read_* functions are used to read data to pandas, the to_* methods are used to store data. The to_excel() method stores the data as an excel file. In the example here, the sheet_name is named passengers instead of the default Sheet1. By setting index=False the row index labels are not saved in the spreadsheet.
to_*
to_excel()
sheet_name
index=False
The equivalent read function read_excel() will reload the data to a DataFrame:
read_excel()
In [7]: titanic = pd.read_excel('titanic.xlsx', sheet_name='passengers')
In [8]: titanic.head() Out[8]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S [5 rows x 12 columns]
I’m interested in a technical summary of a DataFrame
In [9]: titanic.info() <class 'pandas.core.frame.DataFrame'> 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
The method info() provides technical information about a DataFrame, so let’s explain the output in more detail:
info()
It is indeed a DataFrame.
There are 891 entries, i.e. 891 rows.
Each row has a row label (aka the index) with values ranging from 0 to 890.
index
The table has 12 columns. Most columns have a value for each of the rows (all 891 values are non-null). Some columns do have missing values and less than 891 non-null values.
non-null
The columns Name, Sex, Cabin and Embarked consists of textual data (strings, aka object). The other columns are numerical data with some of them whole numbers (aka integer) and others are real numbers (aka float).
Name
Sex
Cabin
Embarked
integer
float
The kind of data (characters, integers,…) in the different columns are summarized by listing the dtypes.
The approximate amount of RAM used to hold the DataFrame is provided as well.
Getting data in to pandas from many different file formats or data sources is supported by read_* functions.
Exporting data out of pandas is provided by different to_*methods.
The head/tail/info methods and the dtypes attribute are convenient for a first check.
head
tail
info
For a complete overview of the input and output possibilities from and to pandas, see the user guide section about reader and writer functions.