In this section of we will be focusing towards pandas package of python. This Python pandas section ranges from beginner to intermediate and to advanced level. Lets get started with python pandas learning

**1) ****Python – Pandas Data Structure**

Pandas in Python deals with three data structures namely Series, Dataframe and Panel. in this section all the three data structures are explained precisely

**2) Create a Series in python – pandas**

Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). In this section we will learn the different ways to create a series in python pandas.

**3) how to Access the elements of a Series in python – pandas**

In This section we will learn how to access the elements of a series in python pandas. We will be learning how to Retrieve Data Using Label and how to Access Data from Series with Position in python pandas.

**4) Format integer column of Dataframe in Python pandas**

In this section we will learn how to format integer column of Dataframe in Python pandas with an example. We will learn rounding off column values

**5) Head and tail function in Python pandas**

In this section we will learn how to get the snap shot of the data, by getting first few rows and last few rows of the data frame i.e Head and Tail function in python

**6) Descriptive or Summary Statistics in python pandas**

Descriptive or summary statistics in python – pandas, can be obtained by using describe function – describe(). Describe Function gives the mean, std and IQR values

**7) Mean Function in Python pandas**

mean() – Mean Function in python pandas is used to calculate the arithmetic mean of a given set of numbers, mean of a data frame ,mean of column and mean of rows

**8) Median Function in Python pandas**

median() – Median Function in python pandas is used to calculate the median or middle value of a given set of numbers, Median of a data frame, median of column and median of rows

**9) Mode Function in Python pandas**

Mode Function in python pandas is used to calculate the mode or most repeated value of a given set of numbers. Mode of a data frame, mode of column and mode of rows.

**10) Harmonic Mean Function in Python**

Harmonic Mean Function in python pandas is used to calculate the harmonic mean of a given set of numbers, Harmonic mean of a data frame, Harmonic mean of column and Harmonic mean of rows

**11) Geometric Mean Function in Python**

Geometric Mean Function in python pandas is used to calculate the geometric mean of a given set of numbers, Geometric mean of a data frame, Geometric mean of column and Geometric mean of rows.

**12) Standard deviation Function in Python pandas**

Standard deviation Function in python pandas is used to calculate standard deviation of a given set of numbers, Standard deviation of a data frame, Standard deviation of column and Standard deviation of rows

**13) Variance Function in Python pandas**

Variance Function in python pandas is used to calculate variance of a given set of numbers, Variance of a data frame, Variance of column and Variance of rows.

**14) How to join (merge) data frames (inner, outer, right, left join) in pandas python**

We can merge two data frames in pandas python by using the merge() function. The different arguments to merge() allow you to perform natural join, left join, right join, and full outer join in pandas.

**15) Apply Functions in Python pandas**

To Apply our own function or some other library’s function, pandas provide three important functions namely pipe(), apply() and applymap()

**16) Rename the column of dataframe in pandas python**

In this section we will learn how to rename the column of dataframe in pandas

In this section we will learn how to reindex in python pandas or change the order of the rows and column in python pandas with the help of reindex() function

Row bind in python pandas – In this section we will learn how to concatenate rows to the python pandas dataframe with append() Function and concat() Function

**19) column bind in python pandas**

Column bind in python pandas. In this section we will learn how to concatenate columns to the python pandas dataframe using concat() Function with example

**20) Assign or add new column to dataframe**

In this section we will learn how to assign or add new column to dataframe in python pandas. assigning a new column the already existing dataframe in python pandas

**21) Group a dataframe in python pandas**

group by function in pandas python: In this section we will learn how to group by in python pandas and perform aggregate functions

**22) Sort the List in python using sort() Function**

Sort the List in python: sort() Function in python sorts the element of given list in either ascending order or descending order.

**23) sort a dataframe in python pandas**

How to sort a dataframe in python pandas by ascending order and by descending order on multiple columns.

**24) sort the dataframe in python pandas by index**

How to sort the dataframe in python pandas by index in terms of ascending and descending order with an example for each using sort_index() method

**25) Cross tab in python pandas**

In this section we will learn how to create cross tab in python pandas ( 2 way cross table or 3 way cross table or contingency table)

**26) Rank the dataframe in python pandas**

In this section we will learn how to rank the dataframe in python pandas by ascending and descending order with maximum rank value, minimum rank value , average rank value and dense rank.

**27) Drop the duplicate row of a dataframe in python pandas**

In this section we will learn how to delete or drop the duplicate row of a dataframe in python pandas with example using drop_duplicates() function

**28) Find the duplicate rows of the dataframe in python pandas**

In this section we will learn how to find the duplicate rows of the dataframe in python pandas with duplicated() Function

**29) Drop or delete the row in python pandas with conditions**

In this section we will learn how to drop or delete the row in python pandas by index, delete row by condition in python pandas and delete the row in python pandas by position.

**30) Drop or delete column in python pandas**

In this section we will learn how to drop or delete column in python pandas by index, drop column in pandas by name and drop column in python pandas by position

**31) Get the maximum value of column in python pandas**

Get the maximum value of column in python pandas : In this section we will learn How to get the maximum value of all the columns in dataframe of python pandas.

**32) Get the minimum value of column in python pandas**

Get the minimum value of column in python pandas : In this section we will learn How to get the minimum value of all the columns in dataframe of python pandas.

**33) select row with maximum and minimum value in python pandas**

In this section we will learn how to select row with maximum and minimum value in python pandas

**34) Get the unique values (rows) of a dataframe in python Pandas**

In this section we will learn how to get the unique values (rows) of a dataframe in python pandas with drop_duplicates() function.

**35) Get the list of column headers or column name in python pandas**

In this section we will learn how to get the list of column headers or column name in python pandas using list() function.

**36) Get unique values of a column in python pandas**

In this section we will learn how to get unique values of a column in python pandas using unique() function

**37) Hierarchical indexing or multiple indexing in python pandas**

Let’s see how to create Hierarchical indexing or multiple indexing in python pandas dataframe. We will be converting a normal dataframe to hierarchical dataframe.

**38) Index, Select and Filter dataframe in pandas python**

Index, Select and Filter dataframe in pandas python – In this section we will learn how to index the dataframe in pandas python

**39) Indexing with iloc, loc and ix in pandas python**

Indexing in pandas python is done mostly with the help of iloc, loc and ix.

**40) Reshape wide to long in pandas python with melt() function**

Reshaping a data from wide to long in pandas python is done with melt() function. melt function in pandas is one of the efficient function to transform the data from wide to long format.

**41) Reshape long to wide in pandas python with pivot function**

Reshaping a data from long to wide in python pandas is done with pivot() function. Pivot() function in pandas is one of the efficient function to transform the data from long to wide format

**42) Reshape using Stack() and unstack() function in Pandas python**

Reshape using Stack() and unstack() function in Pandas python: Reshaping the data using stack() function in pandas converts the data into stacked format .i.e. the column is stacked row wise.

**43) Create Pivot table in Pandas python**

In this section we will be dealing on how to create pivot table from a Pandas dataframe in python with aggregate function – mean ,count and sum.

**44) Scaling and normalizing a column in Pandas python**

Scaling and normalizing a column in pandas python is required, to standardize the data, before we model a data.