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21 Pandas Functions You Should Know for Data Analysis

Essential pandas functions for Exploratory Data Analysis!

Suraj Gurav
Python in Plain English
13 min readAug 24, 2022

Photo by Constantin Shimonenko on Unsplash

Pandas for Analytics!

Exploratory data analysis can be considered the first step of your data analytics task. It essentially gives deeper insights into what is there in the data, whether are there any missing values, is there any correlation between different columns, and so on.

When you work in Python, Pandas is a widely used library for data manipulation and analysis. It starts with importing data into pandas DataFrame and commonly data is imported from .csv files, using pandas.DataFrame.read_csv() method.

Therefore, I listed here the Top 21 Pandas functions and methods which are commonly used for Exploratory Data Analysis.

You can easily navigate to the function you like using the index below:

· df.head()
·
df.tail()
·
df.sample()
·
df.info()
·
df.describe()
·
df.query()
·
df.loc
·
df.iloc
·
df.unique()
·
df.nunique()
·
df.isnull()
·
df.fillna()
·
df.sort_values()
·
df.value_counts()
·
df.nlargest()
·
df.nsmallest()
·
df.copy()
·
df.rename()
·
df.where()
·
df.corr()
·
df.drop()

📍 Note: I’m using a self created Dummy_Sales_Data which you can get on my Github

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Written by Suraj Gurav

Analytics professional and writer. I write about Data Science, Python, SQL & interviews. Join Medium today to get all my articles: https://tinyurl.com/3fehn8pw

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