Selecting columns from Pandas DataFrame
Selecting column or columns from a Pandas DataFrame is one of the most frequently performed tasks while manipulating data. Pandas provides several technique to efficiently retrieve subsets of data from your DataFrame. The Python indexing operators '' and attribute operator '.' allows simple and fast access to DataFrame across a wide range of use cases. Following article will discuss different ways to work with a DataFrame that has a large number of columns.
Create a DataFrame with data
Selecting single column from Pandas DataFrame
You can apply Python selection filters to the DataFrame itself, to select a single column to work with.
Selecting multiple column from Pandas DataFrame
When you select multiple columns from DataFrame, use a list of column names within the selection brackets .
Here the inner square brackets  define a Python list with column names from DataFrame, whereas the outer brackets are used to select the data from a DataFrame .
If you want to get dimensionality of the DataFrame
Selecting range of columns
Select two column with first 3 rows
DataFrame.loc access a group of rows and columns by label(s) or a boolean array .