Search⌘ K
AI Features
Home
Courses
Machine Learning with NumPy, pandas, scikit-learn, and More

Combining

Explore methods to combine pandas DataFrames through concatenation and merging. Understand how to manipulate data effectively by joining rows and columns and handling differing indexes. This lesson helps you write practical code to combine multiple DataFrames for improved data analysis.

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 ...