A Tip A Day – Python Tip #5 – Pandas Concat & Append

In this article we are going to learn about Concat & Append and the comparision between these two functions.

import pandas as pd

Lets take 2 dataframes of fruits.

fruit = {
    'orange' : [3,2,0,1],
    'apple' : [0,3,7,2],
    'grapes' : [7,14,6,15]
}
df1 = pd.DataFrame(fruit)
df1

Output:

	orange	apple	grapes
0	3	0	7
1	2	3	14
2	0	7	6
3	1	2	15

fruit = {
    'grapes' : [13,12,10,2,55,98],
    'mango' : [10,13,17,2,9,76],
    'banana' : [20,23,27,4,0,9],
    'pear' : [21,24,28,51,22,25],
    'pineapple' : [30,33,38,30,36,31]
}
df2 = pd.DataFrame(fruit)
df2

Output:

	grapes	mango	banana	pear	pineapple
0	13	10	20	21	30
1	12	13	23	24	33
2	10	17	27	28	38
3	2	2	4	51	30
4	55	9	0	22	36
5	98	76	9	25	31

df2 = df2.drop(df2.index[2]) 
df2

Output:

	grapes	mango	banana	pear	pineapple
0	13	10	20	21	30
1	12	13	23	24	33
3	2	2	4	51	30
4	55	9	0	22	36
5	98	76	9	25	31

Concat

If both the dataframes contains rows with same index then both the rows will be retained without any change or reset in indexes.

pd.concat((df1, df2), axis = 0)

Output:

	orange	apple	grapes	mango	banana	pear	pineapple
0	3.0	0.0	7	NaN	NaN	NaN	NaN
1	2.0	3.0	14	NaN	NaN	NaN	NaN
2	0.0	7.0	6	NaN	NaN	NaN	NaN
3	1.0	2.0	15	NaN	NaN	NaN	NaN
0	NaN	NaN	13	10.0	20.0	21.0	30.0
1	NaN	NaN	12	13.0	23.0	24.0	33.0
3	NaN	NaN	2	2.0	4.0	51.0	30.0
4	NaN	NaN	55	9.0	0.0	22.0	36.0
5	NaN	NaN	98	76.0	9.0	25.0	31.0

If axis = 1 then concatenates in column wise. If no data available with same index, then NaN will be filled.

For example, the second dataframe df2 does not contain record with index 2. So, After concatenation, the records of df2 will have NaN for index 2 record as df1 has record with index 2.

If both the dataframes contains same columns then both the columns will be retained without any change in column name.

pd.concat((df1, df2), axis = 1)

Output:

	orange	apple	grapes	grapes	mango	banana	pear	pineapple
0	3.0	0.0	7.0	13.0	10.0	20.0	21.0	30.0
1	2.0	3.0	14.0	12.0	13.0	23.0	24.0	33.0
2	0.0	7.0	6.0	NaN	NaN	NaN	NaN	NaN
3	1.0	2.0	15.0	2.0	2.0	4.0	51.0	30.0
4	NaN	NaN	NaN	55.0	9.0	0.0	22.0	36.0
5	NaN	NaN	NaN	98.0	76.0	9.0	25.0	31.0

Append

Append is the specific case of concat which concats the second dataframe’s records at the end of first dataframe.

Append has no axis argument.

Syntax of Append is different from Concat. Append considers the calling dataframe as main object and adds rows to that dataframe from the dataframes that are passed to the function as argument.

If any of the dataframe contains new columns that is not existing in calling dataframe, then it will be added as new column

Syntax: DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)

df1

Output:

	orange	apple	grapes
0	3	0	7
1	2	3	14
2	0	7	6
3	1	2	15

df2

Output:

	grapes	mango	banana	pear	pineapple
0	13	10	20	21	30
1	12	13	23	24	33
3	2	2	4	51	30
4	55	9	0	22	36
5	98	76	9	25	31

df1.append(df2)

Output:

	orange	apple	grapes	mango	banana	pear	pineapple
0	3.0	0.0	7	NaN	NaN	NaN	NaN
1	2.0	3.0	14	NaN	NaN	NaN	NaN
2	0.0	7.0	6	NaN	NaN	NaN	NaN
3	1.0	2.0	15	NaN	NaN	NaN	NaN
0	NaN	NaN	13	10.0	20.0	21.0	30.0
1	NaN	NaN	12	13.0	23.0	24.0	33.0
3	NaN	NaN	2	2.0	4.0	51.0	30.0
4	NaN	NaN	55	9.0	0.0	22.0	36.0
5	NaN	NaN	98	76.0	9.0	25.0	31.0

We will meet with a new tip in Python. Thank you! 👍

Like to support? Just click the heart icon ❤️.

Happy Programming!🎈.