Add new values to a dataframe from a specific index?-CodePudding

In an existing dataframe, how can I add a column with new values in it, but throw these new values from a specific index and increase the dataframe's index size?

As in this example, put the new values from index 2, and go to index 6:

Dataframe:

df = pd.DataFrame({
        'Col1':[1, 1, 1, 1],
        'Col2':[2, 2, 2, 2]
    })
df

Output:

    Col1  Col2
0   1     2
1   1     2
2   1     2
3   1     2

New values:

new_values = [3, 3, 3, 3, 3]

Desired Result:

    Col1  Col2  Col3
0   1     2     NaN
1   1     2     NaN
2   1     2     3
3   1     2     3
4   NaN   NaN   3
5   NaN   NaN   3
6   NaN   NaN   3

CodePudding user response：

The answer by @anarchy works nicely, a similar approach

df = pd.DataFrame({
    'Col1':[1, 1, 1, 1],
    'Col2':[2, 2, 2, 2]
})

specific_index_offset = 2
new_data = [3,3,3,3,3]
new_df = pd.DataFrame(
   data=[[e] for e in new_data], 
   columns=['Col3'],
   index=range(specific_index_offset, specific_index_offset   len(new_data))
)

desired_df = pd.concat([df, new_df], axis=1)

CodePudding user response：

First create a new list and add NaN values that total to the number you want to offset.

Then do a concat.

You can set the series name when you concatnate it and that will be the new column name.

import pandas as pd
import numpy as np

df = pd.DataFrame({
        'Col1':[1, 1, 1, 1],
        'Col2':[2, 2, 2, 2]
    })

new_values = [3, 3, 3, 3, 3]

offset = 2 # set your offset here

new_values = [np.NaN] * offset   new_values # looks like [np.NaN, np.NaN, 3, 3, ... ]

new = pd.concat([df, pd.Series(new_values).rename('Col3')], axis=1)

new looks like this,

    Col1    Col2    Col3
0   1.0     2.0     NaN
1   1.0     2.0     NaN
2   1.0     2.0     3.0
3   1.0     2.0     3.0
4   NaN     NaN     3.0
5   NaN     NaN     3.0
6   NaN     NaN     3.0

CodePudding user response：

You can also try doing some changes from list level

n = 2 #Input here
xs = [None] * 2
new_values = [3, 3, 3, 3, 3]

new= xs new_values


#create new df 
df2 = pd.DataFrame({'col3':new})
df_final = pd.concat([df, df2], axis=1)
print(df_final)

output#

   Col1  Col2  col3
0   1.0   2.0   NaN
1   1.0   2.0   NaN
2   1.0   2.0   3.0
3   1.0   2.0   3.0
4   NaN   NaN   3.0
5   NaN   NaN   3.0
6   NaN   NaN   3.0