Data analysis using Pandas

Pandas are the most popular python library that is used for data analysis. It provides highly optimized performance with back-end source code purely written in C or Python. 

We can analyze data in Pandas with:

• Pandas Series
• Pandas DataFrames

Pandas Series

Series in Pandas is one dimensional(1-D) array defined in pandas that can be used to store any data type.

Creating Pandas Series

Here, Data can be:

1. A Scalar value which can be integerValue, string
2. A Python Dictionary which can be Key, Value pair
3. A Ndarray

Note: Index by default is from 0, 1, 2, ...(n-1) where n is the length of data.  

Create Series from List

 Creating series with predefined index values.

Output:

👁 Create Series from List

👁 Image

Create Pandas Series from Dictionary

Program to Create Pandas series from Dictionary.

Output:

👁 Create Pandas Series from Dictionary

Convert an Array to Pandas Series

Program to Create ndarray series.

Output:

👁 Convert an Array to Pandas Series

Pandas DataFrames

The DataFrames in Pandas is a two-dimensional (2-D) data structure defined in pandas which consists of rows and columns.

Creating a Pandas DataFrame

Here, Data can be:

1. One or more dictionaries
2. One or more Series
3. 2D-numpy Ndarray

Create a Pandas DataFrame from multiple Dictionary

Program to Create a Dataframe with two dictionaries.

Output:

👁 Create a Pandas DataFrame from multiple Dictionary

Convert list of dictionaries to a Pandas DataFrame

Here, we are taking three dictionaries and with the help of from_dict() we convert them into Pandas DataFrame.

Output:

  A B C D
0 5 0 3 3
1 7 9 3 5
2 2 4 7 6

Create DataFrame from Multiple Series

Program to create a dataframe of three Series.

Output:

👁 Create DataFrame from Multiple Series

Convert a Array to Pandas Dataframe

One constraint has to be maintained while creating a DataFrame of 2D arrays - The dimensions of the 2D array must be the same.

Output:

👁 Convert a Array to Pandas Dataframe