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Combining

Understand the key methods to combine pandas DataFrames through concatenation and merging. This lesson helps you write Python code to merge datasets by rows or columns using pd.concat and pd.merge, managing labels and handling overlapping data. Mastering these techniques is essential for effective data manipulation and analysis with pandas.

We'll cover the following...

Chapter Goals:

  • Understand the methods used to combine DataFrame objects

  • Write code for combining DataFrames

In the previous chapter, we discussed the append function for concatenating DataFrame rows. To concatenate multiple DataFrames along either rows or columns, we use the pd.concat function.

The code below shows example usages of pd.concat.

Python 3.5
df1 = pd.DataFrame({'c1':[1,2], 'c2':[3,4]},
                   index=['r1','r2'])
df2 = pd.DataFrame({'c1':[5,6], 'c2':[7,8]},
                   index=['r1','r2'])
df3 = pd.DataFrame({'c1':[5,6], 'c2':[7,8]})
concat = pd.concat([df1, df2], axis=1)
# Newline to separate print statements
print('{}\n'.format(concat))
concat = pd.concat([df2, df1, df3])
print('{}\n'.format(concat))
concat = pd.concat([df1, df3], axis=1)
print('{}\n'.format(concat))

The pd.concat function takes in a list of pandas objects (normally ...