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You can use the following basic syntax to create an empty pandas DataFrame with specific column names:

df = pd.DataFrame(columns=['Col1', 'Col2', 'Col3'])

The following examples shows how to use this syntax in practice.

**Example 1: Create DataFrame with Column Names & No Rows**

The following code shows how to create a pandas DataFrame with specific column names and no rows:

import pandas as pd #create DataFrame df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E']) #view DataFrame df A B C D E

We can use **shape** to get the size of the DataFrame:

#display shape of DataFrame df.shape (0, 5)

This tells us that the DataFrame has **0** rows and **5** columns.

We can also use **list()** to get a list of the column names:

#display list of column names list(df) ['A', 'B', 'C', 'D', 'E']

**Example 2: Create DataFrame with Column Names & Specific Number of Rows**

The following code shows how to create a pandas DataFrame with specific column names and a specific number of rows:

import pandas as pd #create DataFrame df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E'], index=range(1, 10)) #view DataFrame df A B C D E 1 NaN NaN NaN NaN NaN 2 NaN NaN NaN NaN NaN 3 NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN NaN 5 NaN NaN NaN NaN NaN 6 NaN NaN NaN NaN NaN 7 NaN NaN NaN NaN NaN 8 NaN NaN NaN NaN NaN 9 NaN NaN NaN NaN NaN

Notice that every value in the DataFrame is filled with a NaN value.

Once again, we can use **shape** to get the size of the DataFrame:

#display shape of DataFrame df.shape (9, 5)

This tells us that the DataFrame has **9** rows and **5** columns.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

How to Create New Column Based on Condition in Pandas

How to Insert a Column Into a Pandas DataFrame

How to Set Column as Index in Pandas