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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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Creating a MultiIndex DataFrame in Pandas

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Creating DataFrames Cookbook
schedule Mar 10, 2022
Last updated
local_offer PythonPandas
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Using list of tuples

To create a MultiIndex DataFrame in Pandas, we first need to create a MultiIndex object:

index = [("A", "alice"), ("A", "bob"),
("A", "cathy"), ("B", "david"),
("B", "eric")]

multi_index = pd.MultiIndex.from_tuples(index)
multi_index
MultiIndex([('A', 'alice'),
('A', 'bob'),
('A', 'cathy'),
('B', 'david'),
('B', 'eric')])

To create a MultiIndex DataFrame, pass multi_index directly into the DataFrame constructor:

df = pd.DataFrame({"a":[2,3,4,5,6]}, index=multi_index)
df
a
A alice 2
bob 3
cathy 4
B david 5
eric 6

Using arrays

To create a Multi-Index from arrays:

numbers = [3,3,4]
letters = ["A","B","C"]
multi_index = pd.MultiIndex.from_arrays([numbers, letters])
multi_index
MultiIndex([(3, 'A'),
(3, 'B'),
(4, 'C')])

To create a MultiIndex DataFrame, pass multi_index directly into the DataFrame constructor:

df = pd.DataFrame({"a":range(3)}, index=multi_index)
df
a
3 A 0
B 1
4 C 2

Using Cartesian products

To create a MultiIndex using the Cartesian product of two lists:

numbers = [3,4,5]
letters = ["A","B"]
multi_index = pd.MultiIndex.from_product([numbers, letters])
multi_index
MultiIndex([(3, 'A'),
(3, 'B'),
(4, 'A'),
(4, 'B'),
(5, 'A'),
(5, 'B')])

To create a MultiIndex DataFrame, pass multi_index directly into the DataFrame constructor:

df = pd.DataFrame({"a":range(6)}, index=multi_index)
df
a
3 A 0
B 1
4 A 2
B 3
5 A 4
B 5
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Published by Isshin Inada
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