Quelltext der Seite Python Pandas Module Cheatsheet

== Import Pandas Module ==
<syntaxhighlight lang="python3">
import pandas as pd

</syntaxhighlight>

== Creating DataFrames ==

=== From a Dictionary ===
<syntaxhighlight lang="python3">
df1 = pd.DataFrame({
    'name': ['John Smith', 'Jane Doe', 'Joe Schmo'],
    'address': ['123 Main St.', '456 Maple Ave.', '789 Broadway'],
    'age': [34, 28, 51]
})
</syntaxhighlight>'''Note:''' All columns need the same number of elements.

=== From a List ===
<syntaxhighlight lang="python3">
df2 = pd.DataFrame([
    ['John Smith', '123 Main St.', 34],
    ['Jane Doe', '456 Maple Ave.', 28],
    ['Joe Schmo', '789 Broadway', 51]
    ],
    columns=['name', 'address', 'age'])
</syntaxhighlight>

=== From a CSV File ===
<syntaxhighlight lang="python3">
df3 = pd.read_csv('sample.csv')

</syntaxhighlight>

== Viewing DataFrames ==

=== Show top lines ===
<syntaxhighlight lang="python3">
print(df.head())     # print first 5 lines
print(df.head(10)    # print first 10 lines
</syntaxhighlight>

=== Get Informations about DataFrame Data ===
<syntaxhighlight lang="python3">
print(df.info())
</syntaxhighlight>Prints Number of records, and name, datatype and number of filled lines of each individual column.

=== Select one Column ===
Given a dataframe there are two ways to return a column:<syntaxhighlight lang="python3">
column_way1 = df['columnname']

# this only works if the columnname has no special characters and spaces
column_way2 = df.columnname      

</syntaxhighlight>The type of the returned value from a selected column is called a ''Series'', if only one column was selected.<syntaxhighlight lang="python3">
print(type(column_way1))

# returns:
# <class 'pandas.core.series.Series'>


</syntaxhighlight>
=== Select Multiple Columns ===
Selecting columns 3 and 7 from a dataframe with multiple columns:<syntaxhighlight lang="python3">
new_df = df[['column3', 'column7']]
</syntaxhighlight>'''Note:''' Double set of brackets <code>[[]]</code> is mandatory.

The type of the returned value from two selected columns is a ''DataFrame.''<syntaxhighlight lang="python3">
print(type(new_df))

# returns: 
# <class 'pandas.core.frame.DataFrame'>


</syntaxhighlight>

=== Select one Row ===
DataFrames are zero-indexed. This returns the third row from the DataFrame:<syntaxhighlight lang="python3">
new_df = df.iloc[2]
</syntaxhighlight>

=== Select Multiple Rows ===
Rows are zero-indexed:<syntaxhighlight lang="python3">
# from 3rd row (zero-indexed) to but not incl. 7th row
new_df = df.iloc[3:7]

# from zeroth row to but not incl. 4th row
new_df = df.iloc[:4]

# from 3rd to last row to the final row
new_df = df.iloc[-3:]


</syntaxhighlight>

=== Select Multiple Rows with Logical Statement ===
Comparing against single values:<syntaxhighlight lang="python3">new_df = df[df.a_column == 'desired value']
new_df = df[df.a_column != 'desired value']

new_df = df[(df.a_column == 'desired value') |
            (df.a_column == 'another desired value')]

new_df = df[df.b_column > 5]</syntaxhighlight>

=== Select Multiple Rows by List of Values ===
Comparing against a list of values:<syntaxhighlight lang="python3">new_df = df[df.a_column.isin(['desired value','another desired value'])]</syntaxhighlight>

=== Resetting Indices ===
Selecting parts of dataframes leads to non-consecuritve indices. To correct for that:<syntaxhighlight lang="python3"># Creates new dataframe with consecurive indices and a new column holding old indices
new_df = df.reset_index()

# Creates new dataframe with consecurive indices without a column holding old indices
new_df = df.reset_index(drop=True)

# Replaces indices inplace  without extra column for old indices
df.reset_index(inplace=True, drop=True)


</syntaxhighlight>

== Modifying DataFrames ==

=== Adding Columns ===
Add a new column by use of a list of the same length as the existing dataframe:<syntaxhighlight lang="python3">
df['Quantity'] = [100, 150, 50, 35]

</syntaxhighlight>Add a new column that contains the same value in all rows:<syntaxhighlight lang="python3">
df['In Stock'] = True

</syntaxhighlight>

=== Adding a Calculated Column ===
Add a column calculated based on the contents of another column:<syntaxhighlight lang="python3">
df['Margin'] = df.Price - df.Cost

</syntaxhighlight>

=== Perform a Function on a Column ===
<syntaxhighlight lang="python3">
df['Name'] = df.Name.apply(str.upper)
</syntaxhighlight>
[[Kategorie:Python]]