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Pandas Cheat Sheet - Essential Techniques

Simple, expressive, and arguably one of the most important libraries in Python. It makes real-world Data Analysis significantly easier and faster.
For beginners, it’s difficult to remember all the functions and operations of Pandas libraries. To help you with your journey Data Science with Python, we have created this cheat sheet.

The Pandas cheat sheet will help you through the basics of the Pandas library, such as working with DataFrames, Importing and Exporting conventions, Functions, Operations, and Plotting DataFrames in different formats.
Also, if you want to see an illustrated version of this topic with an example on a real-world dataset, you can refer to our Tutorial Blog on Python Pandas.
It is essential to complete our interactive Data Science course to understand Python for data science, as we have explained libraries like Pandas, Numpy, and more.

Download a Printable PDF of this Cheat Sheet

Download a Printable PDF of this Advanced Pandas Cheat Sheet

We need to import the library before we get started.

import pandas as pd

We have two types of data structures in Pandas, Series and DataFrame.

A series is a one-dimensional labeled array that can hold any data type.

DataFrame is a two-dimensional, potentially heterogeneous tabular data structure.
Or we can say Series is the data structure for a single column of a DataFrame.
Now let us see some examples of Series and DataFrames for better understanding.

Series: s = pd.Series([1, 2, 3, 4], index=[‘a’, ‘b’, ‘c’, ‘d’])

= {‘Mobile’: [‘iPhone’, ‘Samsung’, ‘Redmi’], ‘Color’: [‘Red’, ‘White’, ‘Black’], ‘Price’: [High, Medium, Low]}

= pd.DataFrame(

, columns=[‘Mobile’, ‘Color’, ‘Price’])

The Pandas library offers a set of reader functions that can be performed on a wide range of file formats that return a Pandas object. Here we have mentioned a list of reader functions.

pd.read_csv(“filename”)
pd.read_table(“filename”)
pd.read_excel(“filename”)
pd.read_sql(query, connection_object)
pd.read_json(json_string)

Similarly, we have a list of write operations that are useful while writing data into a file.

df.to_csv(“filename”)
df.to_excel(“filename”)
df.to_sql(table_name, connection_object)
df.to_json(“filename”)

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The Pandas library allows us to create fake or test data in order to test our code segments. Check out the examples given below.

df = DataFrame() – Create an empty DataFrame

pd.DataFrame(np.random.rand(4,3)) – 3 columns and 4 rows of random floats
pd.Series(new_series) – Creates a series from an iterablenew_series

df[‘Pet’]  – Return a single column value with the name of the column and the name is ‘Pet’
df[[‘Pet’, ‘Vehicle’]] – Return more than a single column with its name.
df.filter(regex=’TIM’) – Return the column name whose names correspond to the corresponding regular expression patterns.

Here we have mentioned various inbuilt functions and their operations.

df.head(n) – look at the first n rows of the DataFrame.
df.tail(n) – look at the last n rows of the DataFrame.

df.sample(n) – look at randomly n rows from the DataFrame.
df.nlargest(n, ‘value’) – Return and arrange the top n entries.
df.nsmallest(n, ‘value’) – Return and arrange bottom n entries
df[df.HEIGHT > 100] – Return the rows having ‘HEIGHT’ values > 100
df.drop_duplicates() – Remove duplicate rows according to all column
df.shape() – Gives the number of rows and columns.
df.info() – Information of Index, Datatype, and Memory.
df.describe() – Summary statistics for numerical columns.

We want to select and have a look at a chunk of data from our DataFrame. There are two ways of achieving both.
First, select by position, and second, select by label.

• Selecting by position using iloc:

df.iloc[0] – Select first row of the data frame
df.iloc[1] – Select second row of the data frame
df.iloc[-1] – Select last row of the data frame
df.iloc[:,0] – Select first column of the data frame
df.iloc[:,1] – Select second column of the data frame

• Selecting by label, using loc:

df.loc([0], [column labels]) – Select single value by row position & column labels
df.loc[‘row1′:’row3’, ‘column1′:’column3’] – Select and slicing on labels

Another very simple yet useful feature offered by Pandas is the sorting of DataFrame.

df.sort_index() – Sorts by labels along an axis
df.sort_values(column1) – Sorts values by column1 in ascending order
df.sort_values(column2,ascending=False) – Sorts values by column2 in descending order
df.reset_index() – Allows to reset the index back to the default.

Using the groupby technique, you can create a grouping of categories. It will be helpful while applying a function to the categories. This simple yet valuable technique is used widely in data science.

df.groupby(column) – Returns a groupby object for values from one column
df.groupby([column1,column2]) – Returns a groupby object values from multiple columns
df.groupby(column1)[column2].mean() – Returns the mean of the values in column2, grouped by the values in column1
df.groupby(column1)[column2].median() – Returns the median of the values in column2, grouped by the values in column1

For combining different datasets, you can use the merging technique. Parameters to be followed: {‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’

Inner Join: pd.merge(df1, df2, how=’inner’, on=’Apple’)
Outer Join: pd.merge(df1, df2, how=’outer’, on=’Orange’)
Left Join: pd.merge(df1, df2, how=’left’, on=’Animals’)
Right Join: pd.merge(df1, df2, how=’right’, on=’Vehicles’)
Cross Join: df1.merge(df2, how=’cross’)

If we want to rename the labels in the DataFrame, then use the df.rename(index=None, columns=None, axis=None) function; the values of column names and index labels can be replaced.
df.rename(columns={‘ferrari’: ‘FERRARI’, ‘mercedes’: ‘MERCEDES’, ‘bently’: ‘BENTLY’}, inplace=True) – Rename the column name according to its values. If True then the value of the copy is ignored.
df.rename(columns={“”: “a”, “B”: “c”}) – Rename columns by mapping them.
df.rename(index={0: “london”, 1: “newyork”, 2: “berlin”}) – Rename index by mapping them.

To remove duplicate data from the datasets, use duplicates(). It helps to find and eliminate the repeated labels in a DataFrame.

df.duplicated() – method used to identify duplicate rows in a DataFrame
df.index.duplicated – Remove duplicates by index value
df.drop_duplicates() – Remove duplicate rows from the DataFrame

Python Pandas offers a technique to manipulate/reshape a DataFrame and Series in order to change how the data is represented.

pivot = df.pivot(columns=’Vehicles’, values=[‘BRAND’, ‘YEAR’]) –  Create a table with various data types.
pd.melt(df) – Collect columns into a row.
pd.pivot_table(df, values=”10″, index=[“1”, “3”], columns=[“1”]) – Pivoting with the aggregation of numeric data.

The concatenation function is performed when combining objects along a shared index or column.

df = pd.concat() – Allows for a full copy of the data.
df = pd.concat([df3,df1]) – Combining two DataFrame.
df = pd.concat([S3,S1]) – Combining two series.
df = pd.concat([df3,S1], axis=1 ) – Combining the DataFrame and series together.

To refine specific values from a large dataset with multiple conditions, using the filter() method helps filter the DataFrame and returns only the rows or columns that are specified.

df = df.filter(items=[‘City’, ‘Country’]) – Return ‘City’ and ‘Country’ columns from the DataFrame
df = df.filter(like=’tion’, axis=1) – Return columns that present ‘tion’ in names.
df = df.filter(regex=’Quest’) – Return the column name whose names correspond to the corresponding regular expression patterns.
df =  df.query(‘Speed>70’) – Returns the value from a row whose speed is more than 70.

Drop columns: df.drop(columns=[‘column_name’], inplace=True) – Drop rows containing null data in any column.

Filling Data: cbd[“London”].fillna(“Newyork”, inplace = True) – Filling na values in London with Newyork in DataFrame.

Replacing: df_filled.replace([2, 30], [1, 10]) – Replacing more than one value in the DataFrame.

Interpolation: df.interpolate(method =’linear’, limit_direction =’backward’, axis=0) – Interpolate the missing values filled in by columns in the forward direction using the linear method.

There are some special methods available in Pandas that make our calculations easier. Let’s have a look at:

: df.mean() – mean of all columns

: df.median() – median of each column

: df.std() – standard deviation of each column

: df.max() – highest value in each column

: df.min() – lowest value in each column

: df.count() – number of non-null

: df.describe() – Summary statistics for numerical columns

apply those methods in our Product_ReviewDataFrame

While working with a variety of datasets, there is a possibility we have to remove certain data. Using the Dropping() method, specific data can be removed from rows and columns.

Drop Columns: df.drop([‘Nike’], axis=1) – Drop the ‘Nike’ from the columns.

Drop row by index: df.drop([‘Size’], axis=0) – Drop the ‘Size’ from the row.

Dropping the multindex DataFrame: df.drop(index=’offers’, columns=’location’) – Drop the multiple labels  ‘offers’ and ‘location’ from rows and columns, respectively.

In Pandas, indexing returns specific rows and columns of data from the DataFrame.

Retrieving data frame from csv file: detail = pd.read_csv(“employee_db.csv”, index_col =”Contact”)   

Set Index can replace the existing indexes or columns from the DataFrame: detail.set_index(‘Name’) 

Multiple indexing – MultiIndex(levels=[[‘2025-01-01’, ‘2025-01-11’, ‘2025-02-14’], [‘mathew’, ‘linda’]])

Reset Index – df.reset_index(level = 3, inplace = True, col_level = 2)

Data Visualization with Pandas is carried out in the following ways.

• Histogram
• Scatter Plot

Note: Call %matplotlib inline to set up plotting inside the Jupyter notebook.

Histogram: df.plot.hist()
Scatter Plot: df.plot.scatter(x=’column1′,y=’column2′)

We have covered all the basics of Pandas in this cheat sheet. If you want to start learning Pandas in-depth then check out the Python Certification Training by Intellipaat. Not only will you get to learn and implement NumPy with a step by step guidance and support from us, but you will also get to learn some other important libraries in python such as SciPy, NumPy, MatPlotLib, Scikit-learn, Pandas, Lambda function and more. You will also get 24*7 technical support to help you with any and all of your queries, from the experts in the respective technologies here at intellipaat throughout the certification period. Also, you will be provided with Basic Python interview questions asked by the experts during interviews.

Learn effortlessly with our other Python cheat sheets, covering everything from basics to advanced tricks.

1. Python Cheat Sheet
2. NumPy Cheat Sheet
3. Scikit-Learn Cheat Sheet
4. Data Structures with Python Cheat Sheet

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