Python Pandas – Descriptive Statistics ”; Previous Next A large number of methods collectively compute descriptive statistics and other related operations on DataFrame. Most of these are aggregations like sum(), mean(), but some of them, like sumsum(), produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, …}, but the axis can be specified by name or integer DataFrame − “index” (axis=0, default), “columns” (axis=1) Let us create a DataFrame and use this object throughout this chapter for all the operations. Example Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df Its output is as follows − Age Name Rating 0 25 Tom 4.23 1 26 James 3.24 2 25 Ricky 3.98 3 23 Vin 2.56 4 30 Steve 3.20 5 29 Smith 4.60 6 23 Jack 3.80 7 34 Lee 3.78 8 40 David 2.98 9 30 Gasper 4.80 10 51 Betina 4.10 11 46 Andres 3.65 sum() Returns the sum of the values for the requested axis. By default, axis is index (axis=0). Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.sum() Its output is as follows − Age 382 Name TomJamesRickyVinSteveSmithJackLeeDavidGasperBe… Rating 44.92 dtype: object Each individual column is added individually (Strings are appended). axis=1 This syntax will give the output as shown below. Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.sum(1) Its output is as follows − 0 29.23 1 29.24 2 28.98 3 25.56 4 33.20 5 33.60 6 26.80 7 37.78 8 42.98 9 34.80 10 55.10 11 49.65 dtype: float64 mean() Returns the average value Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.mean() Its output is as follows − Age 31.833333 Rating 3.743333 dtype: float64 std() Returns the Bressel standard deviation of the numerical columns. Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.std() Its output is as follows − Age 9.232682 Rating 0.661628 dtype: float64 Functions & Description Let us now understand the functions under Descriptive Statistics in Python Pandas. The following table list down the important functions − Sr.No. Function Description 1 count() Number of non-null observations 2 sum() Sum of values 3 mean() Mean of Values 4 median() Median of Values 5 mode() Mode of values 6 std() Standard Deviation of the Values 7 min() Minimum Value 8 max() Maximum Value 9 abs() Absolute Value 10 prod() Product of Values 11 cumsum() Cumulative Sum 12 cumprod() Cumulative Product Note − Since DataFrame is a Heterogeneous data structure. Generic operations don’t work with all functions. Functions like sum(), cumsum() work with both numeric and character (or) string data elements without any error. Though n practice, character aggregations are never used generally, these functions do not throw any exception. Functions like abs(), cumprod() throw exception when the DataFrame contains character or string data because such operations cannot be performed. Summarizing Data The describe() function computes a summary of statistics pertaining to the DataFrame columns. Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.describe() Its output is as follows − Age Rating count 12.000000 12.000000 mean 31.833333 3.743333 std 9.232682 0.661628 min 23.000000 2.560000 25% 25.000000 3.230000 50% 29.500000 3.790000 75% 35.500000 4.132500 max 51.000000 4.800000 This function gives the mean, std and IQR values. And, function excludes the character columns and given summary about numeric columns. ”include” is the argument which is used to pass necessary information regarding what columns need to be considered for summarizing. Takes the list of values; by default, ”number”. object − Summarizes String columns number − Summarizes Numeric columns all − Summarizes all columns together (Should not pass it as a list value) Now, use the following statement in the program and check the output − Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df.describe(include=[”object”]) Its output is as follows − Name count 12 unique 12 top Ricky freq 1 Now, use the following statement and check the output − Live Demo import pandas as pd import numpy as np #Create a Dictionary of series d = {”Name”:pd.Series([”Tom”,”James”,”Ricky”,”Vin”,”Steve”,”Smith”,”Jack”, ”Lee”,”David”,”Gasper”,”Betina”,”Andres”]), ”Age”:pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]), ”Rating”:pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65]) } #Create a DataFrame df = pd.DataFrame(d) print df. describe(include=”all”) Its output is as follows − Age Name Rating count 12.000000 12 12.000000 unique NaN 12 NaN top NaN Ricky NaN freq NaN 1 NaN mean 31.833333 NaN 3.743333 std 9.232682 NaN 0.661628 min 23.000000 NaN 2.560000 25% 25.000000 NaN 3.230000 50% 29.500000 NaN 3.790000 75% 35.500000 NaN 4.132500 max 51.000000 NaN 4.800000
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