Python Pandas Cheat Sheet

Simple, expressive and arguably one of the most important libraries in Python, not only does it make real-world Data Analysis significantly easier but provides an optimized feature of being significantly fast.
Python Pandas
Commonly, those who just started a Journey of Data Science with Python and Pandas library might find it overwhelming to remember all those functions and their operations, that is when our cheat sheet comes in handy.

Pandas cheat sheet

Pandas cheat sheet will help you through the basics of the Pandas library such as working with DataFrames, Importing and Exporting conventions, Functions, Operations also 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 Pandas.
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Python Pandas Cheat Sheet

Import Convention:

We need to import the library before we get started.

import pandas as pd

Pandas Data Structure:

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


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’])
Data Frame:
data_mobile = {‘Mobile’: [‘iPhone’, ‘Samsung’, ‘Redmi’], ‘Color’: [‘Red’, ‘White’, ‘Black’], ‘Price’: [High, Medium, Low]}
df = pd.DataFrame(data_mobile, columns=[‘Mobile’, ‘Color’, ‘Price’])
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Importing Convention:

Pandas library offers a set of reader functions that can be performed on a wide range of file

pd.read_sql(query, connection_object)

formats which returns a Pandas object. Here we have mentioned a list of reader functions.
Similarly, we have a list of write operations which are useful while writing data into a file.

df.to_sql(table_name, connection_object)

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Create Test/Fake Data:

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

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

Watch this Python Pandas Tutorial for Beginners:


Here we have mentioned various inbuilt functions and their operations.

View DataFrame contents:

df.head(n) – look at first n rows of the DataFrame.
df.tail(n) – look at last n rows of the DataFrame.
df.shape() – Gives the number of rows and columns. – 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 the same.
First, selecting by position and second, selecting by label.

  • Selecting by position using iloc:
df.iloc[0] – Select first row of data frame
df.iloc[1] – Select second row of data frame
df.iloc[-1] – Select last row of data frame
df.iloc[:,0] – Select first column of data frame
df.iloc[:,1] – Select second column of 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


Using groupby technique you can create a grouping of categories and then it can 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 mean of the values in column2, grouped by the values in column1

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There are some special methods available in Pandas which makes our calculation easier. Let’s

Mean:df.mean() – mean of all columns
Median:df.median() – median of each column
Standard Deviation:df.std() – standard deviation of each column
Max:df.max() – highest value in each column
Min:df.min() – lowest value in each column
Count:df.count() – number of non-null values in each DataFrame column
Describe:df.describe() – Summary statistics for numerical columns

apply those methods in our Product_ReviewDataFrame


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′)

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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 free Python interview questions asked by the experts during interviews.
For all other topics get back to the online Python Tutorial.

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