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

how to Select columns values from Pandas DataFrame

Select all rows with first three column

Select first three rows with first four column
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