In [1]: import pandas as pd
Data used for this tutorial:
  • This tutorial uses the Titanic data set, stored as CSV. The data consists of the following data columns:

    • PassengerId: Id of every passenger.

    • Survived: Indication whether passenger survived. 0 for yes and 1 for no.

    • Pclass: One out of the 3 ticket classes: Class 1, Class 2 and Class 3.

    • Name: Name of passenger.

    • Sex: Gender of passenger.

    • Age: Age of passenger in years.

    • SibSp: Number of siblings or spouses aboard.

    • Parch: Number of parents or children aboard.

    • Ticket: Ticket number of passenger.

    • Fare: Indicating the fare.

    • Cabin: Cabin number of passenger.

    • Embarked: Port of embarkation.

    To raw data

How do I read and write tabular data?#

../../_images/02_io_readwrite.svg
  • 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_*.

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  ...     Fare Cabin  Embarked
0              1         0       3  ...   7.2500   NaN         S
1              2         1       1  ...  71.2833   C85         C
2              3         1       3  ...   7.9250   NaN         S
3              4         1       1  ...  53.1000  C123         S
4              5         0       3  ...   8.0500   NaN         S
..           ...       ...     ...  ...      ...   ...       ...
886          887         0       2  ...  13.0000   NaN         S
887          888         1       1  ...  30.0000   B42         S
888          889         0       3  ...  23.4500   NaN         S
889          890         1       1  ...  30.0000  C148         C
890          891         0       3  ...   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  ...     Fare Cabin  Embarked
    0            1         0       3  ...   7.2500   NaN         S
    1            2         1       1  ...  71.2833   C85         C
    2            3         1       3  ...   7.9250   NaN         S
    3            4         1       1  ...  53.1000  C123         S
    4            5         0       3  ...   8.0500   NaN         S
    5            6         0       3  ...   8.4583   NaN         Q
    6            7         0       1  ...  51.8625   E46         S
    7            8         0       3  ...  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.

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.

A check on how pandas interpreted each of the column data types can be done by requesting the pandas dtypes attribute:

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).

Note

When asking for the dtypes, no parentheses () are used! dtypes is an attribute of a DataFrame and Series. Attributes of a DataFrame or Series do not need (). Attributes represent a characteristic of a DataFrame/Series, whereas methods (which require parentheses ()) do something with the DataFrame/Series as introduced in the first tutorial.

  • 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.

The equivalent read function read_excel() will reload the data to a DataFrame:

In [7]: titanic = pd.read_excel("titanic.xlsx", sheet_name="passengers")
In [8]: titanic.head()
Out[8]: 
   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
0            1         0       3  ...   7.2500   NaN         S
1            2         1       1  ...  71.2833   C85         C
2            3         1       3  ...   7.9250   NaN         S
3            4         1       1  ...  53.1000  C123         S
4            5         0       3  ...   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:

    • 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.

    • 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.

    • 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).

    • 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.

REMEMBER

  • 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.

To user guide

For a complete overview of the input and output possibilities from and to pandas, see the user guide section about reader and writer functions.