pandas DataFrame - truncate() | truncate rows or truncate table in pandas |

In this pandas tutorial we will discuss about:

• truncate function pandas or truncate() function.
• truncate table in pandas,
• truncate rows in pandas,

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Introduction

DataFrame in pandas is two dimensional data structure that store data in 2-dimensional format. One dimension refers to a row and second dimension refers to a column, So It will store the data in rows and columns.

 

We can able to create this DataFrame using DataFrame() method. But this is available in pandas module, so we have to import pandas module.

Syntax:

pandas.DataFrame(data)

Where, data is the input dataframe, The data can be a dictionary that stores list of values with specified key.

 

Example: Create pandas dataframe

In this example, we will create dataframe with 4 rows and 4 columns with college data and assign indices through index parameter.

import pandas as pd

#create dataframe from the college data
data= pd.DataFrame({'college_id':['c-001','c-021','c-002','c-004'],

                    'college_name':["vignan university","vvit","RVR - JC","Andhra University"],

                   "college_address":["guntur","guntur","guntur","guntur"],

                    "Total Staff":['1200','3422','5644','670']

                   },index=['one','two','three','four'])

#display the dataframe
print(data)

Output: Our dataframe is created

      college_id       college_name college_address Total Staff
one        c-001  vignan university          guntur        1200
two        c-021               vvit          guntur        3422
three      c-002           RVR - JC          guntur        5644
four       c-004  Andhra University          guntur         670

Lets now learn about pandas truncate table.

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truncate() or pandas truncate table

pandas truncate table or truncate() function is used to remove the rows and columns by selecting only particular rows. truncate means remove.

Syntax:

dataframe_input.truncate(before,after)

where,

1. dataframe_input is the input pandas dataframe
2. before is the index position where, data is truncated before that given position
3. after is the index position where, data is truncated after that position.

Note - Indexing starts with 0.

Example 1: truncate table in pandas example

In this truncate table in pandas example, we are going to truncate before 2nd row and after 3 rd row.

import pandas as pd

#create dataframe from the college data
data= pd.DataFrame({'college_id':['c-001','c-021','c-002','c-004'],

                    'college_name':["vignan university","vvit","RVR - JC","Andhra University"],

                   "college_address":["guntur","guntur","guntur","guntur"],

                    "Total Staff":[1200,3422,5644,670]

                   })

#truncate before second row and after third row
print(data.truncate(before=1, after=2))

Output:

So, only will display second row and third row.

  college_id college_name college_address  Total Staff
1      c-021         vvit          guntur         3422
2      c-002     RVR - JC          guntur         5644

Example 2: truncate rows in pandas example

In this truncate rows in pandas example, we are going to truncate before 1 st row and after 3 rd row

import pandas as pd

#create dataframe from the college data
data= pd.DataFrame({'college_id':['c-001','c-021','c-002','c-004'],

                    'college_name':["vignan university","vvit","RVR - JC","Andhra University"],

                   "college_address":["guntur","guntur","guntur","guntur"],

                    "Total Staff":[1200,3422,5644,670]

                   })

#truncate before first row and after second row
print(data.truncate(before=0, after=1))

Output:

So, only will display first row and second row.

  college_id       college_name college_address  Total Staff
0      c-001  vignan university          guntur         1200
1      c-021               vvit          guntur         3422

This wraps up our session on truncate rows in pandas or truncate values in pandas or pandas truncate dataframe.