To join a list of DataFrames, say dfs, use the pandas.concat(dfs) function that merges an arbitrary number of DataFrames to a single one.
When browsing StackOverflow, I recently stumbled upon the following interesting problem. By thinking about solutions to those small data science problems, you can improve your data science skills, so let’s dive into the problem description.
Problem: Given a list of Pandas DataFrames. How to merge them into a single DataFrame?
Example: You have the list of Pandas DataFrames:
df1 = pd.DataFrame({'Alice' : [18, 'scientist', 24000], 'Bob' : [24, 'student', 12000]})
df2 = pd.DataFrame({'Alice' : [19, 'scientist', 25000], 'Bob' : [25, 'student', 11000]})
df3 = pd.DataFrame({'Alice' : [20, 'scientist', 26000], 'Bob' : [26, 'student', 10000]}) # List of DataFrames
dfs = [df1, df2, df3]
Say, you want to get the following DataFrame:
Alice Bob 0 18 24 1 scientist student 2 24000 12000 0 19 25 1 scientist student 2 25000 11000 0 20 26 1 scientist student 2 26000 10000
You can try the solution quickly in our interactive Python shell:
Exercise: Print the resulting DataFrame. Run the code. Which merging strategy is used?
Method 1: Pandas Concat
This is the easiest and most straightforward way to concatenate multiple DataFrames.
import pandas as pd df1 = pd.DataFrame({'Alice' : [18, 'scientist', 24000], 'Bob' : [24, 'student', 12000]})
df2 = pd.DataFrame({'Alice' : [19, 'scientist', 25000], 'Bob' : [25, 'student', 11000]})
df3 = pd.DataFrame({'Alice' : [20, 'scientist', 26000], 'Bob' : [26, 'student', 10000]}) # list of dataframes
dfs = [df1, df2, df3] df = pd.concat(dfs)
This generates the following output:
print(df) ''' Alice Bob 0 18 24 1 scientist student 2 24000 12000 0 19 25 1 scientist student 2 25000 11000 0 20 26 1 scientist student 2 26000 10000 '''
The resulting DataFrames contains all original data from all three DataFrames.
Method 2: Reduce + DataFrame Merge
The following method uses the reduce function to repeatedly merge together all dictionaries in the list (no matter its size). To merge two dictionaries, the df.merge() method is used. You can use several merging strategies—in the example, you use "outer":
import pandas as pd df1 = pd.DataFrame({'Alice' : [18, 'scientist', 24000], 'Bob' : [24, 'student', 12000]})
df2 = pd.DataFrame({'Alice' : [19, 'scientist', 25000], 'Bob' : [25, 'student', 11000]})
df3 = pd.DataFrame({'Alice' : [20, 'scientist', 26000], 'Bob' : [26, 'student', 10000]}) # list of dataframes
dfs = [df1, df2, df3] # Method 2
from functools import reduce
df = reduce(lambda df1, df2: df1.merge(df2, "outer"), dfs)
This generates the following output:
print(df) ''' Alice Bob 0 18 24 1 scientist student 2 24000 12000 3 19 25 4 25000 11000 5 20 26 6 26000 10000 '''
You can find a discussion of the different merge strategies here. If you’d use the parameter "inner", you’d obtain the following result:
Alice Bob 0 scientist student
Where to Go From Here?
Enough theory, let’s get some practice!
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