Append multiple pandas data frames at once

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

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.

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())

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