Создайте среднее и стандартное отклонение данных серии Pandas
Standard Deviation is the square root of the Variance. The Standard Deviation denoted by sigma is a measure of the spread of numbers. In pandas, the std() function is used to find the standard Deviation of the series.
The mean can be simply defined as the average of numbers. In pandas, the mean() function is used to find the mean of the series.
Example 1 : Finding the mean and Standard Deviation of a Pandas Series.
# importing the moduleimport pandas as pd # creating a seriess = pd.Series(data = [5, 9, 8, 5, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 5, 3]) # displaying the seriesprint(s) |
Output :
Finding the mean of the series using the mean() function.
# finding the meanprint(s.mean()) |
Output :
Finding the standard deviation of the series using the std() function.
# finding the Standard deviationprint(s.std()) |
Output :
Example 2 : Finding the mean and Standard Deviation of a Pandas DataFrame.
# importing the moduleimport pandas as pd # creating a dataframe df = pd.DataFrame({"ID":[114, 345, 157788, 5626], "Product":["shirt", "trousers", "tie", "belt"], "Color":["White", "Black", "Red", "Brown"], "Discount":[10, 10, 10, 10]}) # displaying the DataFrameprint(df) |
Output :
Finding the mean of the DataFrame using the mean() function.
# finding the meanprint(df.mean()) |
Output :
Finding the standard deviation of the DataFrame using the std() function.
# finding the Standard deviationprint(df.std()) |
Output :
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- Python pandas-series
- Python-pandas
- Python