Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.
Pandas DataFrame.truncate() function is used to truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.
Syntax: DataFrame.truncate(before=None, after=None, axis=None, copy=True)
Parameter :
before : Truncate all rows before this index value.
after : Truncate all rows after this index value.
axis : Axis to truncate. Truncates the index (rows) by default.
copy : Return a copy of the truncated section.Returns : The truncated Series or DataFrame.
Example #1: Use DataFrame.truncate() function to truncate some entries before and after the passed labels of the given dataframe.
# importing pandas as pd import pandas as pd # Creating the DataFrame df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71], 'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'], 'Age':[14, 25, 55, 8, 21]}) # Create the index index_ = pd.date_range('2010-10-09 08:45', periods = 5, freq ='H') # Set the index df.index = index_ # Print the DataFrame print(df) |
Output :
Now we will use DataFrame.truncate() function to truncate the entries before ‘2010-10-09 09:45:00’ and after ‘2010-10-09 11:45:00’ in the given dataframe.
# return the truncated dataframe result = df.truncate(before = '2010-10-09 09:45:00', after = '2010-10-09 11:45:00') # Print the result print(result) |
Output :
As we can see in the output, the DataFrame.truncate() function has successfully truncated the entries before and after the passed labels in the given dataframe.
Example #2: Use DataFrame.truncate() function to truncate some entries before and after the passed labels of the given dataframe.
# importing pandas as pd import pandas as pd # Creating the DataFrame df = pd.DataFrame({"A":[12, 4, 5, None, 1], "B":[7, 2, 54, 3, None], "C":[20, 16, 11, 3, 8], "D":[14, 3, None, 2, 6]}) # Create the index index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5'] # Set the index df.index = index_ # Print the DataFrame print(df) |
Output :
Now we will use DataFrame.truncate() function to truncate the entries before ‘Row_3’ and after ‘Row_4’ in the given dataframe.
# return the truncated dataframe result = df.truncate(before = 'Row_3', after = 'Row_4') # Print the result print(result) |
Output :
As we can see in the output, the DataFrame.truncate() function has successfully truncated the entries before and after the passed labels in the given dataframe.
