Check if a Pandas DataFrame is Empty
You can use the attribute df.empty to check whether it's empty or not. True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name' : []})
if df.empty:
print('DataFrame is empty!')
else:
print('Not empty!')
DataFrame is empty!
If you have only NaNs in your DataFrame, it is not considered as empty.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name' : [np.nan]})
if df.empty:
print('DataFrame is empty!')
else:
print('Not empty!')
Not empty!
Using len()
You can use the len function for checking DataFrame is empty or not. It's much faster than df.empty.
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill']
df['Promoted'] = [True, False,True]
df['Marks'] = [82, 38, 63]
df.drop(df.index, inplace=True)
if len(df.index) == 0:
print('DataFrame is empty!')
else:
print('Not empty!')
DataFrame is empty!
Related Topics
- Creating an empty Pandas DataFrame
- How to check if a column exists in Pandas Dataframe
- How to delete column from pandas DataFrame
- How to select multiple columns from Pandas DataFrame
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- Selecting rows in pandas DataFrame based on conditions
- How to Drop rows in DataFrame by conditions on column values
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- Change the order of columns in Pandas dataframe
- Concatenate two columns into a single column in pandas dataframe
- How to count the number of rows and columns in a Pandas DataFrame
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- How to iterate over rows in a DataFrame in Pandas
- How to drop rows/columns of Pandas DataFrame whose value is NaN
- How to Export Pandas DataFrame to a CSV File
- Convert list of dictionaries to a pandas DataFrame
- How to set a particular cell value in pandas DataFrame