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 .
Select all column with first row
