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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 DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising 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 DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising 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 DataFrame with random numbers in Pandas

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Creating DataFrames Cookbook
schedule Jul 1, 2022
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To create a DataFrame with random numbers in Pandas, use one of NumPy's functions that generate random numbers:

  • np.random.randint(~) for random integers

  • np.random.default_rng().uniform(~) for random floats

DataFrame with random integers

First, we generate a 2 by 3 NumPy array of random integers:

arr_random = np.random.randint(low=2, high=10, size=(2,3))
arr_random
array([[8, 7, 2],
[2, 3, 6]])

Here, the lower bound is 2 (inclusive) and the upper bound is 10 (exclusive).

We can then initialise a DataFrame using arr_random like so:

pd.DataFrame(arr_random, columns=["A","B","C"], index=["a","b"])
A B C
a 4 4 2
b 3 7 2

DataFrame with random floats

First, we generate a 2 by 3 NumPy array of random floats:

arr_random = np.random.default_rng().uniform(low=5,high=10,size=[2,3])
arr_random
array([[7.04518804, 9.09625941, 5.29815702],
[7.77653952, 9.22222284, 9.0035309 ]])

Note the following:

  • all floats are larger than or equal to 5.

  • all floats are less than 10.

To initialise a DataFrame using arr_random:

pd.DataFrame(arr_random, columns=["A","B","C"])
A B C
0 7.927711 5.560577 5.918331
1 8.487705 9.111051 9.699249
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Published by Isshin Inada
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