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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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Combining multiple Series to form a DataFrame in Pandas

schedule Aug 11, 2023
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To combine multiple Series to form a DataFrame in Pandas, use the concat(~) method.

Horizontally stacking Series

To horizontally stack two Series:

s1 = pd.Series(['a','b'])
s2 = pd.Series(['c','d'])
df = pd.concat([s1, s2], axis=1) # returns a DataFrame
df
0 1
0 a c
1 b d

Here, both s1 and s2 share the same index ([0,1]) so they could be stacked horizontally (axis=1).

Case when index labels do not align

In the case when the index labels don't perfectly align, we would end up with missing values:

s1 = pd.Series(['a','b'], index=["A","B"])
s2 = pd.Series(['c','d'], index=["B","C"])
pd.concat([s1, s2], axis=1)
0 1
A a NaN
B b c
C NaN d

Supplement - assigning new column labels

The resulting DataFrame will have the default integer indices as the column labels, so you may want to assign new labels like so:

df.columns = ["A","B"]
df
A B
a a NaN
b b c
c NaN d

Vertically stacking Series

To vertically stack multiple Series to form a new DataFrame, use concat(~):

s1 = pd.Series(['a','b'])
s2 = pd.Series(['c','d'])
df = pd.concat([s1, s2], axis=0).to_frame()
df
0
0 a
1 b
0 c
1 d

Note the following:

  • axis=0 for concat(~) means that we want to stack the Series vertically, as opposed to horizontally.

  • the return type of concat(~) in this case is a Series, and so to convert this Series into a DataFrame, we use to_frame().

robocat
Published by Isshin Inada
Edited by 0 others
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