#PYTHON PANDAS DATAFRAME PART-6 || # How to create dataframe IN PYTHON

#PYTHON PANDAS DATAFRAME
Pandas (all lowercase) is a popular Python-based data analysis toolkit which can be imported using import pandas as pd. It presents a diverse range of utilities, ranging from parsing multiple file formats to converting an entire data table into a NumPy matrix array. This makes pandas a trusted ally in data science and machine learning.

Similar to NumPy, pandas deals primarily with data in 1-D and 2-D arrays; however, pandas handles the two differently.

Series
In pandas, 1-D arrays are referred to a series. A series is created through the pd.Series constructor, which has a lot of optional arguments. The most common argument is data, which specifies the elements of the series.

Similar to the NumPy arrays, a pandas series also uses the dtype keyword for manual casting.

DataFrame
A DataFrame is simply a 2-D array. It can be created through the pd.DataFrame constructor, which takes in essentially the same arguments as pd.Series. However, while a series could be constructed from a scalar (representing a single value Series), a DataFrame cannot.

The index (row) and column labels of a DataFrame can be defined in the constructor.

# HOW to create dataframe
# by using list

import pandas as pd
a = pd.DataFrame([['Ankit','Rituraj','Aman','Dev','Kiran'],[1,2,3,4,5]], index=[9,8,7,6,5], columns=['A','R','A','D','K'])
print(a)

# by using disc.

b={'Name': ['Ankit','Rituraj','Aman','Dev','Kiran'],'Salary': [50000,32000,40000,30000,30000]}
c = pd.DataFrame(b)
print(c)