In [1]: import pandas as pd
- Titanic data
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.
0for yes and1for no.Pclass: One out of the 3 ticket classes: Class
1, Class2and Class3.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.
How do I read and write tabular data?#
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 pandasDataFrame. pandas supports many different file formats or data sources out of the box (csv, excel, sql, json, parquet, …), each of them with the prefixread_*.
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 thehead()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 brackets are used!
dtypes is an attribute of a DataFrame and Series. Attributes
of a DataFrame or Series do not need brackets. Attributes
represent a characteristic of a DataFrame/Series, whereas
methods (which require brackets) 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, theto_*methods are used to store data. Theto_excel()method stores the data as an excel file. In the example here, thesheet_nameis named passengers instead of the default Sheet1. By settingindex=Falsethe 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
DataFrameIn [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 aDataFrame, 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 891non-nullvalues.The columns
Name,Sex,CabinandEmbarkedconsists of textual data (strings, akaobject). The other columns are numerical data with some of them whole numbers (akainteger) and others are real numbers (akafloat).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/infomethods and thedtypesattribute are convenient for a first check.
For a complete overview of the input and output possibilities from and to pandas, see the user guide section about reader and writer functions.