Filling Empty Python Dataframe Using Loops

Lets say I want to create and fill an empty dataframe with values from a loop. import pandas as pd import numpy as np years = [2013, 2014, 2015] dn=pd.DataFrame() for year in yea

Solution 1:

import pandas as pd

years = [2013, 2014, 2015]
dn = []
for year in years:
    df1 = pd.DataFrame({'Incidents': [ 'C', 'B','A'],
                 year: [1, 1, 1 ],
                }).set_index('Incidents')
    dn.append(df1)
dn = pd.concat(dn, axis=1)
print(dn)

yields

           2013  2014  2015
Incidents                  
C             1     1     1
B             1     1     1
A             1     1     1

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Note that calling pd.concat once outside the loop is more time-efficient than calling pd.concat with each iteration of the loop.

Each time you call pd.concat new space is allocated for a new DataFrame, and all the data from each component DataFrame is copied into the new DataFrame. If you call pd.concat from within the for-loop then you end up doing on the order of n**2 copies, where n is the number of years.

If you accumulate the partial DataFrames in a list and call pd.concat once outside the list, then Pandas only needs to perform n copies to make dn.

Solution 2:

As far as I know you should avoid to add line by line to the dataframe due to speed issue

What I usually do is:

l1 = []
l2 = []

for i in range(n):
   compute value v1
   compute value v2
   l1.append(v1)
   l2.append(v2)

d = pd.DataFrame()
d['l1'] = l1
d['l2'] = l2