Using the loc indexer with a multi-indexed pandas DataFrame or Series allows you to subset the data based on one or more levels of the index. Here are the general steps to follow when using loc with multi-index:
Specify the first index level(s) you want to select by passing a tuple of values to the loc indexer. For example, if you have a DataFrame with a two-level index, and you want to select all rows where the first index level is “A”, you can use df.loc[(“A”, ), :]. Note the comma after the “A” inside the tuple, which indicates that you want to select all values in the second index level.
If you want to select based on more than one index level, you can pass a tuple of tuples to the loc indexer. For example, if you have a three-level index and you want to select all rows where the first level is “A” and the second level is “B”, you can use df.loc[(“A”, “B”), :].
You can also use slice objects to select a range of index values. For example, if you want to select all rows where the first index level is between “A” and “C”, you can use df.loc[(“A”:”C”), :].
Here is an example code snippet to demonstrate the use of loc with multi-index:
import pandas as pd
# Create a DataFrame with a multi-index
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame({'data': [1, 2, 3, 4]}, index=index)
# Select all rows where the first index level is "A"
print(df.loc[("A", ), :])
# Select all rows where the first level is "A" and the second level is 2
print(df.loc[("A", 2), :])
# Select all rows where the first level is "A" or "B"
print(df.loc[("A":"B"), :])
# Select all rows where the second level is 2
print(df.loc[(slice(None), 2), :])
You can also pass a boolean array to loc to select rows based on a condition. The boolean array must have the same length as the number of rows in the DataFrame. For example, if you want to select all rows where the value in the “data” column is greater than 2, you can use df.loc[df[“data”] > 2, :].
You can also use loc to select specific columns based on both the index and column labels. For example, if you have a DataFrame with a multi-index and columns “A”, “B”, and “C”, and you want to select all rows where the first index level is “A” and only columns “B” and “C”, you can use df.loc[(“A”, ), [“B”, “C”]].
Here is an example code snippet to demonstrate the use of loc with multi-index to select specific columns:
import pandas as pd
# Create a DataFrame with a multi-index and multiple columns
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame({'data1': [1, 2, 3, 4], 'data2': [10, 20, 30, 40]}, index=index)
# Select all rows where the first index level is "A" and only columns "data1" and "data2"
print(df.loc[("A", ), ["data1", "data2"]])
You can also use loc to modify values in the DataFrame. For example, if you want to set all values in the “data1” column to 0 where the second index level is 1, you can use df.loc[(slice(None), 1), “data1”] = 0.
Here is an example code snippet to demonstrate how to modify values using loc with multi-index:
import pandas as pd
# Create a DataFrame with a multi-index and multiple columns
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame({'data1': [1, 2, 3, 4], 'data2': [10, 20, 30, 40]}, index=index)
# Set all values in the "data1" column to 0 where the second index level is 1
df.loc[(slice(None), 1), "data1"] = 0
print(df)
These are the basic methods for using loc with multi-index in pandas. By combining these methods, you can easily filter, slice, and modify multi-indexed data in a DataFrame or Series.