how to get columns in pandas | Different Ways to Get Python Pandas Column Names

In Pandas, you can get columns from a DataFrame using various methods. Here are some common ways to access columns:

Using Column Names: You can directly access a column by its name. If your DataFrame is named df, and you want to access a column named "column_name," you can do it like this:

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df['column_name']
For example, if you have a column named "Age," you can access it as df['Age'].

Using Dot Notation: If your column names are valid Python variable names (no spaces or special characters), you can also use dot notation to access columns:

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For example, if you have a column named "Age," you can access it as df.Age.

Selecting Multiple Columns: To select multiple columns, you can pass a list of column names:

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df[['column1', 'column2']]
This will return a DataFrame containing only the specified columns.

Using loc and iloc: You can also use the loc and iloc methods to access columns. loc allows you to select columns by label, and iloc allows you to select columns by integer location.

Using loc:

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To select multiple columns with loc:

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Using iloc:

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To select multiple columns with iloc:

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Note that in the above examples, : is used to select all rows.

Accessing Columns as a Series: When you extract a single column from a DataFrame, it is returned as a Pandas Series. If you want to work with it as a DataFrame with a single column, you can wrap it in double square brackets:

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df[['column_name']]
This will return a DataFrame with one column.

Checking for Column Existence: To check if a column exists in the DataFrame, you can use the in operator:

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if 'column_name' in df:
# Column exists
These methods should cover most scenarios for accessing columns in a Pandas DataFrame. Remember to replace 'column_name' with the actual name of the column you want to access.