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Function Application Continued Aggregation Groupby...

Aggregation(group by) -  A groupby operation involves some combination of splitting the object, applying a function, and combining the results.  This can be used to group large amounts of data and compute operations on these groups. Groupby function works when you have categorical data as we have in the weather data below. Here 'weather', 'Year'and 'State'are all categorical. Groupby operation involves one of the following operations. Splitting the Object Applying a function    Combining the results gp = df.groupby(['Weather','State']) for name,group in gp: print(name) print(group) Output:- 1. Splitting the object -  import pandas as pd weather_data = {'Weather': ['Rainy', 'Stormy', 'Sunny', 'Cloudy', 'Rainy', 'Sunny', 'Cloudy', 'Rainy', 'Stormy', 'Cloudy', 'Sunny', 'Sunny'], 'State': ['CG...
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Function Application Continued(transform)...

Transform - This function is used to change the data elements, lets say some data are not how you wannt them to be to process it, usually used during the data preparation step in machine learning. Example1:- import pandas as pd import numpy as np df = pd.DataFrame({'A':range(3),'B':range(1,4)}) print(df.transform(lambda x:x+1)) Output:- A B 0 1 2 1 2 3 2 3 4 Explanation:- range(3) gives a series of number from 0 to 2 range(1,4) gives a series of number from 1 to 3 lambda operator takes each element x and adds 1 to it. Example2:- import pandas as pd import numpy as np s = pd.Series(range(3)) print(s.transform([np.sqrt,np.exp])) Output:- sqrt exp 0 0.000 1.000 1 1.000 2.718 2 1.414 7.389 Explanation:- range(3) gives a series of number from 0 to 2 s.transform function applies two operation(square root and exponential) on each element of series s.
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Descriptive Statistics - min(), max()

A. min() - Find out the minimum and maximum out of a given set of data. Example:- import pandas as pd df = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:\{'q1':44900,'q2':46100,'q3':57000,'q4':59000},2018:{'q1':54500,'q2':51000}}) print(df) Output:- 2016 2017 2018 q1 34500 44900 54500.0 q2 56000 46100 51000.0 q3 47000 57000 NaN q4 49000 59000 NaN 1. print(df.min()) Output:- 2016 34500.0 2017 44900.0 2018 51000.0 Explanation: min() finds the minimum among the indexes for each column and axis 0 by default. 2. print(df.min(axis=1)) Output:- q1 34500.0 q2 46100.0 q3 47000.0 q4 49000.0 Explanation: min(axis=1) finds the minimum among the columns for each indexes. import pandas as pd df2 = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'B...
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