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Using Objects/DictionariesUsing a Dictionary of arraysUsing Pandas SeriesUsing Standard ListsUsing 2D array Related
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chevron_leftCreating 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 Pandas DataFrame

schedule Aug 12, 2023
Last updated
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Pandas●Python
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To create a Pandas DataFrame, use the DataFrame(~) constructor.

Using Objects/Dictionaries

To create a DataFrame using objects:

people_age = {
"Alex": 30,
"Bob": 35,
"Cathy": 25
}
people_height = {
"Alex": 175,
"Bob": 170,
"Cathy": 160
}
data = pd.DataFrame({"Age":people_age, "Height":people_height})
print(data)
       Age   Height
Alex   30    175
Bob    35    170
Cathy  25    160

Using a Dictionary of arrays

To create a DataFrame using a dictionary of arrays:

df = pd.DataFrame({"A":[3,4], "B":[5,6]}, index=["a","b"])
df
A B
a 3 5
b 4 6

Using Pandas Series

To create a DataFrame using Series:

s_one = pd.Series([3,4], index=["a","b"])
s_two = pd.Series([5,6], index=["a","b"])
df = pd.DataFrame({"A":s_one, "B":s_two})
df
   A  B
a  3  5
b  4  6

Using Standard Lists

To create a DataFrame using Python's standard lists:

values = [[1,2,3], [4,5,6]]
column_labels = ["A", "B", "C"]
indices = ["D", "E"]
data = pd.DataFrame(values, index=indices, columns=column_labels)
print(data)
  A B C
D 1 2 3
E 4 5 6

Using 2D array

To create a DataFrame using 2D array:

df = pd.DataFrame([[3,4],[5,6]], index=["a","b"], columns=["A","B"])
df
A B
a 3 4
b 5 6

Related

Pandas | DataFrame constructor
Pandas DataFrame(~) constructor is used to initialise a new DataFrame.
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Published by Isshin Inada
Edited by 0 others
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