Merging DataFrames with pandas¶
Here, you'll learn all about merging pandas DataFrames. You'll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which. You'll also learn about ordered merging, which is useful when you want to merge DataFrames whose columns have natural orderings, like date-time columns.
import pandas as pd
Merging company DataFrames¶
Suppose your company has operations in several different cities under several different managers. The DataFrames revenue and managers contain partial information related to the company. That is, the rows of the city columns don't quite match in revenue and managers (the Mendocino branch has no revenue yet since it just opened and the manager of Springfield branch recently left the company).
revenue = pd.read_csv("revenue.csv")
managers = pd.read_csv("managers.csv")
print(revenue)
print(managers)
The DataFrames have been printed in the IPython Shell. If you were to run the command combined = pd.merge(revenue, managers, on='city'), how many rows would combined have?
combined = pd.merge(revenue, managers, on="city")
print(combined)
Correct! Since the default strategy for pd.merge() is an inner join, combined will have 2 rows.
The merge command is the key learning objective of this tutorial. The merging operation at its simplest takes a left dataframe (the first argument), a right dataframe (the second argument), and then a merge column name, or a column to merge “on”. In the output/result, rows from the left and right dataframes are matched up where there are common values of the merge column specified by “on”.
An inner merge, (or inner join) keeps only the common values in both the left and right dataframes for the result.
Merging on a specific column¶
You expect your company to grow and, eventually, to operate in cities with the same name on different states. As such, you decide that every branch should have a numerical branch identifier. Thus, you add a branch_id column to both DataFrames. Moreover, new cities have been added to both the revenue and managers DataFrames as well.
revenue = pd.read_csv("revenue_branch_id.csv")
managers = pd.read_csv("managers_branch_id.csv")
print(revenue)
print(managers)
Using pd.merge(), merge the DataFrames revenue and managers on the 'city' column of each
merge_by_city = pd.merge(revenue, managers, on="city")
print(merge_by_city)
Merge the DataFrames revenue and managers on the 'branch_id' column of each.
merge_by_id = pd.merge(revenue, managers, on="branch_id")
print(merge_by_id)
Well done! Notice that when you merge on 'city', the resulting DataFrame has a peculiar result: In row 2, the city Springfield has two different branch IDs. This is because there are actually two different cities named Springfield - one in the State of Illinois, and the other in Missouri. The revenue DataFrame has the one from Illinois, and the managers DataFrame has the one from Missouri. Consequently, when you merge on 'branch_id', both of these get dropped from the merged DataFrame.
Merging on columns with non-matching labels¶
We continue working with the revenue & managers DataFrames from before. This time, someone has changed the field name 'city' to 'branch' in the managers table. Now, when you attempt to merge DataFrames, an exception is thrown:
revenue = pd.read_csv("revenue_branch_id_2.csv")
managers = pd.read_csv("managers_branch_id_2.csv")
print(revenue)
print(managers)
`pd.merge(revenue, managers, on='city')
Traceback (most recent call last):
...
pd.merge(revenue, managers, on='city')
...
KeyError: 'city' `
Given this, it will take a bit more work for you to join or merge on the city/branch name. You have to specify the left_on and right_on parameters in the call to pd.merge().
combined = pd.merge(revenue, managers, left_on="city", right_on="branch")
print(combined)
Great work! It is important to pay attention to how columns are named in different DataFrames.
Merging on multiple columns¶
Another strategy to disambiguate cities with identical names is to add information on the states in which the cities are located. To this end, you add a column called state to both DataFrames from the preceding exercises.
Our goal in this exercise is to use pd.merge() to merge DataFrames using multiple columns (using 'branch_id', 'city', and 'state' in this case).
revenue = pd.read_csv("revenue_branch_id.csv")
managers = pd.read_csv("managers_branch_id.csv")
# Add 'state' column to revenue
revenue["state"] = ["TX", "CO", "IL", "CA"]
# Add 'state' column to managers
managers["state"] = ["TX", "CO", "CA", "MO"]
print(revenue)
print(managers)
# Merge revenue & managers on 'branch_id', 'city', & 'state'
combined = pd.merge(revenue, managers, on=["branch_id", "city", "state"])
print(combined)
Excellent work!
Other Merge Types¶
There are three different types of merges available in Pandas. These merge types are common across most database and data-orientated languages (SQL, R, SAS) and are typically referred to as “joins”. If you don’t know them, learn them now.
- Inner Merge / Inner join – The default Pandas behaviour, only keep rows where the merge “on” value exists in both the left and right dataframes.
- Left Merge / Left outer join – (aka left merge or left join) Keep every row in the left dataframe. Where there are missing values of the “on” variable in the right dataframe, add empty / NaN values in the result.
- Right Merge / Right outer join – (aka right merge or right join) Keep every row in the right dataframe. Where there are missing values of the “on” variable in the left column, add empty / NaN values in the result.
- Outer Merge / Full outer join – A full outer join returns all the rows from the left dataframe, all the rows from the right dataframe, and matches up rows where possible, with NaNs elsewhere.
The merge type to use is specified using the how parameter in the merge command, taking values left, right, inner (default), or outer.
Venn diagrams are commonly used to exemplify the different merge and join types.
Left & right merging on multiple columns¶
We now have, in addition to the revenue and managers, a DataFrame sales that summarizes units sold from specific branches (identified by city and state but not branch_id).
By merging revenue and sales with a right merge, we can identify the missing revenue values. Here, we don't need to specify left_on or right_on because the columns to merge on have matching labels.
managers = pd.read_csv("managers_branch_id_2.csv")
managers["state"] = ["TX", "CO", "CA", "MO"]
sales = pd.read_csv("sales.csv")
print(sales)
revenue_and_sales = pd.merge(revenue, sales, how="right", on=["city", "state"])
print(revenue_and_sales)
By merging sales and managers with a left merge, we can identify the missing manager. Here, the columns to merge on have conflicting labels, so we must specify left_on and right_on. In both cases, we're looking to figure out how to connect the fields in rows containing Springfield.
sales_and_managers = pd.merge(
sales, managers, how="left", left_on=["city", "state"], right_on=["branch", "state"]
)
print(sales_and_managers)
Well done! This is a good way to retain both entries of Springfield.
Merging DataFrames with outer join¶
The merged DataFrames contain enough information to construct a DataFrame with 5 rows with all known information correctly aligned and each branch listed only once. We will try to merge the merged DataFrames on all matching keys (which computes an inner join by default). We can compare the result to an outer join and also to an outer join with restricted subset of columns as keys.
Merge sales_and_managers with revenue_and_sales
merge_default = pd.merge(sales_and_managers, revenue_and_sales)
print(merge_default)
Merge sales_and_managers with revenue_and_sales using how='outer'
merge_outer = pd.merge(sales_and_managers, revenue_and_sales, how="outer")
print(merge_outer)
Merge sales_and_managers with revenue_and_sales only on ['city','state'] using an outer join.
merge_outer_on = pd.merge(
sales_and_managers, revenue_and_sales, how="outer", on=["city", "state"]
)
print(merge_outer_on)
Fantastic work! Notice how the default merge drops the Springfield rows, while the default outer merge includes them twice.
Ordered merges¶
Using merge_ordered()¶
This exercise uses DataFrames austin and houston that contain weather data from the cities Austin and Houston respectively.
Weather conditions were recorded on separate days and we need to merge these two DataFrames together such that the dates are ordered. To do this, we'll use pd.merge_ordered(). Note the order of the rows before and after merging.
austin = pd.read_csv("austin.csv")
houston = pd.read_csv("houston.csv")
print(austin)
print(houston)
Perform an ordered merge on austin and houston using pd.merge_ordered()
tx_weather = pd.merge_ordered(austin, houston)
print(tx_weather)
Perform another ordered merge on austin and houston.
This time, specify the keyword arguments on='date' and suffixes=['_aus','_hus'] so that the rows can be distinguished.
tx_weather_suff = pd.merge_ordered(
austin, houston, on="date", suffixes=["_aus", "_hus"]
)
print(tx_weather_suff)
Perform a third ordered merge on austin and houston.
This time, in addition to the on and suffixes parameters, specify the keyword argument fill_method='ffill' to use forward-filling to replace NaN entries with the most recent non-null entry
tx_weather_ffill = pd.merge_ordered(
austin, houston, on="date", suffixes=["_aus", "_hus"], fill_method="ffill"
)
print(tx_weather_ffill)
Well done! Notice how after using a fill method, there are no more NaN entries.
Conclusion¶
Hurray! You have come to the end of the tutorial. In this tutorial, you learned to merge DataFrames using the merge() function of pandas library. Towards the end, you also practiced the special function merge_ordered().
This tutorial used the following sources to help write it: