Pandas Cheat Sheet for Data Science in Python
Evelyn Clarke
⚡ Smart Summary
Pandas cheat sheets condense the library’s most used commands into one page. This reference groups them by task: installing, building Series and DataFrames, importing files, selecting rows, cleaning data, applying functions and exporting results.
What is a Pandas Cheat Sheet?
The Pandas library ships hundreds of functions, yet only a small subset appears in everyday work. A Pandas cheat sheet is a one-page reference listing those commands, grouped by the task they perform.
It covers the Series and DataFrame structures, importing and exporting files, selecting and ordering data, cleaning values, and applying functions. Each section below pairs a short explanation with runnable code, so the page also works as a refresher for your data analysis skills.
👉 Download the PDF of Cheat Sheet here
How to Install and Import Pandas
Two commands set Pandas up, and both run in a terminal or a Jupyter Notebook cell.
Step 1) Install Pandas
Run this once per environment; prefix it with an exclamation mark in a notebook cell.
pip install pandas
Step 2) Import Pandas
Import the package under the alias pd, which every example here assumes.
import pandas as pd
You can now call Pandas functions to manipulate, analyse and clean data. The sections below list them in the order you normally reach for them.
Pandas Data Structures
Pandas has two data structures, Series and DataFrame. Both are labelled arrays and both can hold any data type. The only difference is dimensionality: a Series is a one-dimensional array, and a DataFrame is a two-dimensional array built from NumPy arrays underneath.
| Feature | Series | DataFrame |
|---|---|---|
| Dimensions | One-dimensional | Two-dimensional |
| Holds | A single labelled column | Many columns, mixed dtypes |
| Constructor | pd.Series() | pd.DataFrame() |
1. Series
It is a one-dimensional labelled array. It can hold any data type, and None becomes a missing value.
s = pd.Series([2, -4, 6, 3, None], index=['A', 'B', 'C', 'D', 'E'])
2. DataFrame
It is a two-dimensional labelled array. It can hold any data type and different sizes of columns.
data = {'RollNo' : [101, 102, 75, 99],
'Name' : ['Mithlesh', 'Ram', 'Rudra', 'Mithlesh'],
'Course' : ['Nodejs', None, 'Nodejs', 'JavaScript']
}
df = pd.DataFrame(data, columns=['RollNo', 'Name', 'Course'])
df.head()
The printable cheat sheet below collects the same commands on one page.
Importing Data
Pandas can read many file types straight into a DataFrame. Every reader below returns one, and read_csv() is the most used.
# Import a CSV file pd pd.read_csv(filename) # Import a TSV file pd.read_table(filename) # Import a Excel file pd pd.read_excel(filename) # Import a SQL table/database pd.read_sql(query, connection_object) # Import a JSON file pd.read_json(json_string) # Import a HTML file pd.read_html(url) # From clipboard to read_table() pd.read_clipboard() # From dict pd.DataFrame(dict)
Selection
You can select elements by their location or by their index label. The same square-bracket syntax picks rows, columns and slices from both structures.
1. Series
# Accessing one element from Series s['D'] # Accessing all elements between two given indices s['A':'C'] # Accessing all elements from starting till given index s[:'C'] # Accessing all elements from given index till end s['B':]
2. DataFrame
# Accessing one column df df['Name'] # Accessing rows from after given row df[1:] # Accessing till before given row df[:1] # Accessing rows between two given rows df[1:2]
Selecting by Boolean Indexing and Setting
Use iloc and iat for integer positions, and loc and at for labels.
1. By Position
df.iloc[0, 1] df.iat[0, 1]
2. By Label
df.loc[[0], ['Name']]
3. By Label/Position
df.loc[2] # Both are same
df.iloc[2]
4. Boolean Indexing
A boolean mask keeps only rows where the condition is True. The second example adds the NOT operator ~, so it also returns values that are not above 1.
# Series s where value is > 1 s[(s > 0)] # Series s where value is <-2 or >1 s[(s < -2) | ~(s > 1)] # Use filter to adjust DataFrame df[df['RollNo']>100] # Set index a of Series s to 6 s['D'] = 10 s.head()
Data Cleaning
For Python data-cleaning cheat sheet purposes, you can perform the following operations:
- Rename columns using the
rename()method. - Update values using the
at[]oriat[]method to access and modify specific elements. - Create a copy of a Series or DataFrame using the
copy()method. - Check for NULL values using the
isnull()method, and drop them using thedropna()method. - Check for duplicate values using the
duplicated()method. Drop them using thedrop_duplicates()method. - Replace NULL values using the
fillna()method with a specified value. - Replace values using the
replace()method. - Sort values using the
sort_values()method. - Rank values using the
rank()method.
# Renaming columns df.columns = ['a','b','c'] df.head() # Mass renaming of columns df = df.rename(columns={'RollNo': 'ID', 'Name': 'Student_Name'}) # Or use this edit in same DataFrame instead of in copy df.rename(columns={'RollNo': 'ID', 'Name': 'Student_Name'}, inplace=True) df.head() # Counting duplicates in a column df.duplicated(subset='Name') # Removing entire row that has duplicate in given column df.drop_duplicates(subset=['Name']) # You can choose which one keep - by default is first df.drop_duplicates(subset=['Name'], keep='last') # Checks for Null Values s.isnull() # Checks for non-Null Values - reverse of isnull() s.notnull() # Checks for Null Values df df.isnull() # Checks for non-Null Values - reverse of isnull() df.notnull() # Drops all rows that contain null values df.dropna() # Drops all columns that contain null values df.dropna(axis=1) # Replaces all null values with 'Guru99' df.fillna('Guru99') # Replaces all null values with the mean s.fillna(s.mean()) # Converts the datatype of the Series to float s.astype(float) # Replaces all values equal to 6 with 'Six' s.replace(6,'Six') # Replaces all 2 with 'Two' and 6 with 'Six' s.replace([2,6],['Two','Six']) # Drop from rows (axis=0) s.drop(['B', 'D']) # Drop from columns(axis=1) df.drop('Name', axis=1) # Sort by labels with axis df.sort_index() # Sort by values with axis df.sort_values(by='RollNo') # Ranking entries df.rank() # s1 is pointing to same Series as s s1 = s # s_copy of s, but not pointing same Series s_copy = s.copy() # df1 is pointing to same DataFrame as df df1 = s # df_copy of df, but not pointing same DataFrame df_copy = df.copy()
Note that s1 = s only creates a second name for the same object, never a copy — that is why copy() follows it.
Retrieving Information
You can perform these operations to retrieve information:
- Use the
shapeattribute to get the number of rows and columns. - Use the
head()ortail()method to obtain the first or last few rows as a sample. - Use the
info(),describe()ordtypesmethod to obtain information about the data type, count, mean, standard deviation, minimum and maximum values. - Use the
count(),min(),max(),sum(),mean()andmedian()methods to obtain specific statistical information for values. - Use the
loc[]method to obtain a row. - Use the
groupby()method to apply the GROUP BY function to group similar values in a column of a DataFrame.
1. Basic information
# Counting all elements in Series len(s) # Counting all elements in DataFrame len(df) # Prints number of rows and columns in dataframe df.shape # Prints first 10 rows by default, if no value set df.head(10) # Prints last 10 rows by default, if no value set df.tail(10) # For counting non-Null values column-wise df.count() # For range of index df df.index # For name of attributes/columns df.columns # Index, Data Type and Memory information df.info() # Datatypes of each column df.dtypes # Summary statistics for numerical columns df.describe()
2. Summary
# For adding all values column-wise df.sum() # For min column-wise df.min() # For max column-wise df.max() # For mean value in number column df.mean() # For median value in number column df.median() # Count non-Null values s.count() # Count non-Null values df.count() # Return Series of given column df['Name'].tolist() # Name of columns df.columns.tolist() # Creating subset df[['Name', 'Course']] # Return number of values in each group df.groupby('Name').count()
Applying Functions
apply() pushes a function across every value. Alignment rules then decide what happens when two objects share only part of their index.
# Define function f = lambda x: x*5 # Apply this function on given Series - For each value s.apply(f) # Apply this function on given DataFrame - For each value df.apply(f)
1. Internal Data Alignment
# NA values for indices that don't overlap s2 = pd.Series([8, -1, 4], index=['A', 'C', 'D']) s + s2
2. Arithmetic Operations with Fill Methods
# Fill values that don't overlap
s.add(s2, fill_value=0)
3. Filter, Sort and Group By
These functions can be used for filtering, sorting and grouping Series and DataFrame objects.
# Filter rows where column is greater than 100 df[df['RollNo']>100] # Filter rows where 70 < column < 101 df[(df['RollNo'] > 70) & (df['RollNo'] < 101)] # Sorts values in ascending order s.sort_values() # Sorts values in descending order s.sort_values(ascending=False) # Sorts values by RollNo in ascending order df.sort_values('RollNo') # Sorts values by RollNo in descending order df.sort_values('RollNo', ascending=False)
Exporting Data
Every writer below mirrors a reader from the importing section, so a file can make the round trip without extra tooling.
# Export as a CSV file df df.to_csv(filename) # Export as a Excel file df df.to_excel(filename) # Export as a SQL table df df.to_sql(table_name, connection_object) # Export as a JSON file df.to_json(filename) # Export as a HTML table df.to_html(filename) # Write to the clipboard df.to_clipboard()
Colab of Cheat Sheet
Every command here is runnable in the companion notebook: Pandas Cheat Sheet – Python for Data Science.ipynb.
