How do I assign index values to a level of Multi index Data Frame?

pandas multiindex
pandas multiindex tutorial
pandas multiindex to single index
pandas multiindex get level
pandas groupby multiindex
pandas hierarchical columns
pandas add level to multiindex
pandas loc multiindex

I have a Multi index Data Frame. However, I wanted to change its first level to a certain list of index values. Suppose its first level is initially [2,4,1], I want to change it to [1,2,100]. What is the simplest way to achieve it? My current approach would involve, reset_index, change column values and set index again.

One way is to create a dictionary of the old values to the replacement values, then iterate through the index as tuples replacing the values, and assign the new index back to the DataFrame:

new_vals = {2: 1, 4: 2, 1: 100}
df.index = pd.MultiIndex.from_tuples([(new_vals[tup[0]], tup[1]) for tup in df.index.to_list()])

(This assumes your MultiIndex has only 2 levels, for every additional level that you want to keep you'd need to add tup[2] etc into the list comprehension.)

MultiIndex / advanced indexing — pandas 0.25.1 documentation, This is sometimes called chained assignment and should be avoided. Hierarchical / Multi-level indexing is very exciting as it opens the door to some quite of dimensions in lower dimensional data structures like Series (1d) and DataFrame (2d). You can slice with a 'range' of values, by providing a slice of tuples. Hierarchical / Multi-level indexing is very exciting as it opens the door to some quite sophisticated data analysis and manipulation, especially for working with higher dimensional data. In essence, it enables you to store and manipulate data with an arbitrary number of dimensions in lower dimensional data structures like Series (1d) and

Use df.reindex()

data.reindex([1,2,100])

Hierarchical Indexing, For example, if you need to select all values from 2010, you'll need to do some Each extra level in a multi-index represents an extra dimension of data; taking with a MultiIndex this is as easy as adding another column to the DataFrame :. By using set_index (), you can assign an existing column of pandas.DataFrame to index (row label). Setting unique names for index makes it easy to select elements with loc and at. pandas.DataFrame.set_index — pandas 0.22.0 documentation Here, the following contents will be described.

Use rename:

Setup

import pandas as pd

index = pd.MultiIndex.from_tuples([(e, i) for i, e in enumerate([2, 4, 1])])
df = pd.DataFrame([1, 2, 3], index=index)

print(df)

Output (of setup)

     0
2 0  1
4 1  2
1 2  3

Code

new_index = [1, 2, 100]
new_vals = dict(zip(df.index.levels[0], new_index))
print(df.rename(new_vals, level=0))

Output

       0
1   0  1
2   1  2
100 2  3

How to Use MultiIndex in Pandas to Level Up Your Analysis, These index values can be numbers, from 0 to infinity. FrozenList of index name of multi-level index DataFrame value “None” earlier has been replaced by the names of the four columns we assigned to be our new index. These are by far the most common ways to index data. These are four function which help in getting the elements, rows, and columns from a DataFrame. Indexing a Dataframe using indexing operator []: Indexing operator is used to refer to the square brackets following an object. The .loc and .iloc indexers also use the indexing operator to make

Assigning values to Pandas Multiindex DataFrame by index level , Series (and dictionaries) can be used just like functions with map and apply ( thanks to @normanius for improving the syntax): df['A'] = pd. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator. drop bool, default True. Delete columns to be used as the new index. append bool, default False. Whether to append columns to existing index. inplace bool, default False. Modify the DataFrame in place (do not create a new object). verify_integrity bool, default

pandas Multi-index and groupbys, Learn about the pandas multi-index or hierarchical index for DataFrames The index of a DataFrame is a set that consists of a label for each row. The above tells you that your DataFrame df now has a MultiIndex with two levels, the first function allows you to get an overview of all numerical variables: Convert a MultiIndex to an Index of Tuples containing the level values. is_lexsorted (self) Return True if the codes are lexicographically sorted. sortlevel (self[, level, ascending, …]) Sort MultiIndex at the requested level. droplevel (self[, level]) Return index with requested level(s) removed. swaplevel (self[, i, j]) Swap level i with

Using Hierarchical Indexes With Pandas, There are many ways to declare multiple indexes on a DataFrame We also see the values of our indexes being populated into levels. A level� Alter Index or MultiIndex name. Able to set new names without level. Defaults to returning new index. Length of names must match number of levels in MultiIndex. Parameters name label or list of labels. Name(s) to set. inplace bool, default False. Modifies the object directly, instead of creating a new Index or MultiIndex. Returns Index

Comments
  • Welcome to SO. Please review How to Ask and create a minimal reproducible example