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Creating an empty Pandas DataFrame with column names can be done by providing the column names as a list to the DataFrame constructor. The DataFrame will be empty, but it will contain the specified columns, each of which will have a default data type assigned to it. We can then populate the DataFrame with data by adding rows to it one at a time.
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)