Overview:
- The Series class of the python library pandas, implements a one-dimensional, mutable container for storing heterogeneous elements using the numpy.ndarray as the underlying storage.
- In addition to providing a versatile one dimensional container, the pandas.Series provides numerous computational methods for analyzing the data.
- The subtraction operation is a binary operation. A binary operation consumes two values to produce a new value. In case of subtraction between two pandas.Series instances, one element of the Series is subtracted from the another producing a new Series.
- To subtract two pandas.Series instances, the function Series.sub() is used. The subtraction operator “–“ can as well be used for the same purpose. In the similar way to subtract a DataFrame instance from another, the DataFrame.sub() function can be used.
- While using the sub() function explicitly, it is possible to replace any occurrence of None, using the parameter fill_value.
Example:
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# Example Python program that subtracts one series from another import pandas as pds import numpy as np
# Generate ten random integers between 1 and 20 np.random.seed(1); randomNumbers = np.random.randint(1, 20, 10); # Create a pandas series series1 = pds.Series(randomNumbers);
# Generate ten random integers between 1 to 5 np.random.seed(1); randomNumbers = np.random.randint(1, 5, 10); # Create a pandas series series2 = pds.Series(randomNumbers);
# Subtract a pandas Series instance from another series3 = series1.sub(series2); print("Series1:"); print(series1);
print("Series2:"); print(series2);
print("Series3:"); print(series3); |
Output:
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Series1: 0 6 1 12 2 13 3 9 4 10 5 12 6 6 7 16 8 1 9 17 dtype: int64 Series2: 0 2 1 4 2 1 3 1 4 4 5 2 6 4 7 2 8 4 9 1 dtype: int64 Series3: 0 4 1 8 2 12 3 8 4 6 5 10 6 2 7 14 8 -3 9 16 dtype: int64 |
Example - Subtracting a pandas Series from another while replacing None values with zero:
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# Example Python program that subtracts one series from another # where one or more elements are None. import pandas as pds import numpy as np
# Create pandas.Series instances lookAndSaySeries = pds.Series([1, 11, 21, 1211, 111221, 312211]); series2Subtract = pds.Series([1, 0, None, 2, None, 4]);
# While subtracting replace any None with zero newSeries = lookAndSaySeries.sub(series2Subtract, fill_value=0);
print("Look and Say Series:"); print(lookAndSaySeries);
print("Series to Subtract:"); print(series2Subtract);
print("New series after subtraction:"); print(newSeries); |
Output:
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Look and Say Series: 0 1 1 11 2 21 3 1211 4 111221 5 312211 dtype: int64 Series to Subtract: 0 1.0 1 0.0 2 NaN 3 2.0 4 NaN 5 4.0 dtype: float64 New series after subtraction: 0 0.0 1 11.0 2 21.0 3 1209.0 4 111221.0 5 312207.0 dtype: float64 |
Example:
Any python sequence of same length like a Python tuple, a Python list can be subtracted from a pandas.Series, as given in the Python example program below.
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# Example Python program that subtracts the elements # of a tuple from a pandas.Series import pandas as pds import numpy as np
numbers = [1, 1, 4, 8, 22, 51];
# Create a pandas.Series from a Python List series1 = pds.Series(numbers);
# Create a python tuple toSubtract = (1, 2, 3, 4, 9, 20);
# Subtract a tuple from a series resultantSeries = series1.sub(toSubtract);
print("Contents of the pandas.Series instance:"); print(series1);
print("Contents of the Python tuple:"); print(series1);
print("Resultant pandas.Series after subtracting the tuple:"); print(resultantSeries); |
Output:
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Contents of the pandas.Series instance: 0 1 1 1 2 4 3 8 4 22 5 51 dtype: int64 Contents of the Python tuple: 0 1 1 1 2 4 3 8 4 22 5 51 dtype: int64 Resultant pandas.Series after subtracting the tuple: 0 0 1 -1 2 1 3 4 4 13 5 31 dtype: int64 |