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Python Pandas
Cheat sheet
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This covers some of the most commonly used functions and operations in Pandas:
Importing and Exporting Data
Here is a quick Python Pandas cheatsheet that covers some of the most
common functions and operations you will use when working with Pandas:
Importing Pandas
To use Pandas, you will first need to import the library:
import pandas as pd
Reading a CSV file
You can read a CSV file into a Pandas DataFrame using the read_csv function:
df = pd.read_csv('filename.csv')
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Displaying the DataFrame
To view the data in a DataFrame, you can use the head function to display the
first few rows:
df.head()
You can also use the tail function to display the last few rows:
df.tail()
To display the entire DataFrame, you can simply print it:
print(df)
Selecting Columns
You can select a single column of a DataFrame by using the [] operator and the
column name:
df['column_name']
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You can also select multiple columns by passing a list of column names:
df[['column_1', 'column_2']]
Filtering Rows
You can filter the rows of a DataFrame using a boolean expression. For example,
to select all rows where the value in the 'age' column is greater than 30:
df[df['age'] > 30]
Sorting Data
You can sort the rows of a DataFrame by one or more columns using the
sort_values function. For example, to sort the DataFrame by the 'age' column in
ascending order:
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df.sort_values(by='age')
To sort in descending order, set the ascending parameter to False:
df.sort_values(by='age', ascending=False)
Grouping Data
You can group a DataFrame by one or more columns and apply a function to
each group using the groupby function. For example, to group the DataFrame by
the 'gender' column and compute the mean of each group:
df.groupby('gender').mean()
Joining DataFrames
You can join two DataFrames using the merge function. For example, to join two
DataFrames on the 'user_id' column:
df1.merge(df2, on='user_id')
Pivot Tables
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You can create a pivot table from a DataFrame using the pivot_table function.
For example, to create a pivot table with the 'gender' column as the rows, the
'country' column as the columns, and the 'age' column as the values:
df.pivot_table(index='gender', columns='country', values='age')
Handling Missing Values
Pandas includes functions for handling missing values. To drop rows with
missing values:
df.dropna()
To fill missing values with a specific value, you can use the fillna function:
df.fillna(value=0)
You can also fill missing values with the mean of the column using the fillna
function and the mean function:
df.fillna(df.mean())
Converting Data Types
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