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Machine Learning with NumPy, pandas, scikit-learn, and More

Combining

Explore how to combine pandas DataFrames using methods like concat and merge. Learn to concatenate rows or columns and merge based on common columns to handle data efficiently for analysis tasks.

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 a list of DataFrames) to concatenate. The function also takes in numerous keyword ...