Python | Pandas DataFrame.truncate
Last Updated :
21 Feb, 2019
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.
import pandas as pd
df = pd.DataFrame({ 'Weight' :[ 45 , 88 , 56 , 15 , 71 ],
'Name' :[ 'Sam' , 'Andrea' , 'Alex' , 'Robin' , 'Kia' ],
'Age' :[ 14 , 25 , 55 , 8 , 21 ]})
index_ = pd.date_range( '2010-10-09 08:45' , periods = 5 , freq = 'H' )
df.index = index_
print (df)
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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.
result = df.truncate(before = '2010-10-09 09:45:00' , after = '2010-10-09 11:45:00' )
print (result)
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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.
import pandas as pd
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 ]})
index_ = [ 'Row_1' , 'Row_2' , 'Row_3' , 'Row_4' , 'Row_5' ]
df.index = index_
print (df)
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Output :
Now we will use DataFrame.truncate()
function to truncate the entries before ‘Row_3’ and after ‘Row_4’ in the given dataframe.
result = df.truncate(before = 'Row_3' , after = 'Row_4' )
print (result)
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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.
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