Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.to_frame() function is used to convert the given series object to a dataframe.
Syntax: Series.to_frame(name=None)
Parameter :
name : The passed name should substitute for the series name (if it has one).Returns : data_frame : DataFrame
Example #1: Use Series.to_frame() function to convert the given series object to a dataframe.
# importing pandas as pdimport pandas as pd  # Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Moscow'])  # Create the Datetime Indexdidx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='W',                      periods = 6, tz = 'Europe/Berlin')   # set the indexsr.index = didx  # Print the seriesprint(sr) |
Output :
Now we will use Series.to_frame() function to convert the given series object to a dataframe.
# convert to dataframesr.to_frame() |
Output :
As we can see in the output, the Series.to_frame() function has successfully converted the given series object to a dataframe.
Example #2: Use Series.to_frame() function to convert the given series object to a dataframe.
# importing pandas as pdimport pandas as pd  # Creating the Seriessr = pd.Series([19.5, 16.8, 22.78, 20.124, 18.1002])  # Print the seriesprint(sr) |
Output :
Now we will use Series.to_frame() function to convert the given series object to a dataframe.
# convert to dataframesr.to_frame() |
Output :
As we can see in the output, the Series.to_frame() function has successfully converted the given series object to a dataframe.
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