CODEWITHSUNDEEP
pythonpandasappend
user3664020
user3664020

Reputation: 3020

Append multiple pandas data frames at once

I am trying to find some way of appending multiple pandas data frames at once rather than appending them one by one using 

df.append(df)

Let us say there are 5 pandas data frames t1, t2, t3, t4, t5. How do I append them at once? Something equivalent of 

df = rbind(t1,t2,t3,t4,t5)

Upvotes: 106

Views: 211767

Answers (4)

Fariliana Eri
Fariliana Eri

Reputation: 301

if the columns each data frame is different you can add for to append :

#list dataframe you want to append
frame = [t1, t2, t3, t4, t5]

#new dataframe to store append result
myDataFrame = pd.DataFrame()

for df in frame:
    myDataFrame = myDataFrame.append(df)

make sure append success by checking the myDataFrame lenght with :

len(myDataFrame)

if all columns in the data frame is the same or the columns difference each data frame will not be a concern as long as the number of columns of each data frame is the same you can use pd.concat(dataframe) as mention by jezrael.

for more info about append and concat, please click link below : Merge, join, concatenate and compare in Pandas

Upvotes: 5

Shailesh Shukla
Shailesh Shukla

Reputation: 21

Suppose we have one main Excel file in which we have 3 sub-sheets named by p1,p2 & p3

We can read all the sheets together as follows:

d = pd.read_excel('p1.xlsx',sheet_name=['p1','p2','p3'])

Now to combine all sheets in one data frame, we can use the following:

df=pd.concat(d[frame] for frame in d.keys())

Upvotes: 2

jezrael
jezrael

Reputation: 862591

I think you can use concat:

print pd.concat([t1, t2, t3, t4, t5])

Maybe you can ignore_index:

print pd.concat([t1, t2, t3, t4, t5], ignore_index=True)

More info in docs.

Upvotes: 129

tfv
tfv

Reputation: 6259

Have you simply tried using a list as argument of append? Or am I missing anything?

import numpy as np
import pandas as pd

dates = np.asarray(pd.date_range('1/1/2000', periods=8))
df1 = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
df2 = df1.copy()
df3 = df1.copy()
df = df1.append([df2, df3])

print df

Upvotes: 91

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