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.from_csv() function is used to read a csv file into a series. It is preferable to use the more powerful pandas.read_csv() for most general purposes.
Syntax: Series.from_csv(path, sep=’, ‘, parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)
Parameter :
path : string file path or file handle / StringIO
sep : Field delimiter
parse_dates : Parse dates. Different default from read_table
header : Row to use as header (skip prior rows)
index_col : Column to use for index
encoding : a string representing the encoding to use if the contents are non-ascii
infer_datetime_format : If True and parse_dates is True for a column, try to infer the datetime format based on the first datetime stringReturns : Series
For this example we have used a CSV file. To download click here
Example #1: Use Series.from_csv() function to read the data from the given CSV file into a pandas series.
# importing pandas as pd import pandas as pd # Read the data into a series sr = pd.Series.from_csv('nba.csv') # Print the first 10 rows of series print(sr.head(10)) |
Output :
As we can see in the output, the Series.from_csv() function has successfully read the csv file into a pandas series.
Example #2 : Use Series.from_csv() function to read the data from the given CSV file into a pandas series. Use the 1st column as an index of the series object.
# importing pandas as pd import pandas as pd # Read the data into a series sr = pd.Series.from_csv('nba.csv', index_col = 1) # Print the first 10 rows of series print(sr.head(10)) |
Output :
As we can see in the output, the Series.from_csv() function has successfully read the csv file into a pandas series.
