{"id":723,"date":"2023-12-16T18:22:14","date_gmt":"2023-12-16T18:22:14","guid":{"rendered":"https:\/\/emorah.com\/story\/?p=723"},"modified":"2023-12-16T18:22:16","modified_gmt":"2023-12-16T18:22:16","slug":"fresco-play-python-pandas-hands-on-solution-t-factor","status":"publish","type":"post","link":"https:\/\/emorah.com\/story\/fresco-play\/fresco-play-python-pandas-hands-on-solution-t-factor\/","title":{"rendered":"Fresco Play Python Pandas Hands- on Solution || T Factor"},"content":{"rendered":"\n

Fresco Play Python Pandas Hands- on Solution || T Factor<\/strong><\/p>\n\n\n\n

Fresco Play Python Pandas Hands-on Solution – T Factor (Course ID:- 55937)<\/h1>\n\n\n\n

In Python Pandas(Course Id:- 55937), There are 8 Hands-On Questions Available. The Solutions are\u00a0<\/p>\n\n\n\n

<\/p>\n\n\n\n

  1. Data Structures in Pandas Solution.<\/li><\/ol>\n\n\n\n

    Code: -#Write your code here
    import pandas as pd
    import numpy as np
    heights_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
    heights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
    print(heights_A.shape)<\/p>\n\n\n\n

    TASK 2<\/h1>\n\n\n\n

    weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
    weights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
    print(weights_A.dtype)<\/p>\n\n\n\n

    TASK 3<\/h1>\n\n\n\n

    df_A = pd.DataFrame()
    df_A[‘Student_height’] = heights_A
    df_A[‘Student_weight’] = weights_A
    print(df_A.shape)<\/p>\n\n\n\n

    TASK 4<\/h1>\n\n\n\n

    my_mean = 170.0
    my_std = 25.0
    np.random.seed(100)
    heights_B = pd.Series(np.random.normal(loc = my_mean, scale = my_std, size = 5))
    heights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

    my_mean1 = 75.0
    my_std1 = 12.0
    weights_B = pd.Series(np.random.normal(loc = my_mean1, scale = my_std1, size = 5))
    weights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
    print(heights_B.mean())<\/p>\n\n\n\n

    TASK 5<\/h1>\n\n\n\n

    df_B = pd.DataFrame()
    df_B[‘Student_height’] = heights_B
    df_B[‘Student_weight’] = weights_B
    print(df_B.columns)<\/p>\n\n\n\n

    TASK 6<\/h1>\n\n\n\n

    data = {
    ‘ClassA’: df_A,
    ‘ClassB’: df_B
    }
    p = pd.Panel.from_dict(data)
    print(p.shape)<\/p>\n\n\n\n

    1. Data Cleaning Solutions – Python Pandas<\/li><\/ol>\n\n\n\n

      Code: -#Write your code here
      import pandas as pd
      import numpy as np
      height_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
      height_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
      weight_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
      weight_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
      df_A = pd.DataFrame()
      df_A[‘Student_height’] = height_A
      df_A[‘Student_weight’] = weight_A<\/p>\n\n\n\n

      df_A.loc[‘s3’] = np.nan
      df_A.loc[‘s5’][1] = np.nan<\/p>\n\n\n\n

      df_A2 = df_A.dropna(how = ‘any’)
      print(df_A2)<\/p>\n\n\n\n

      1. Data Merging Hands – On(2) Solution: Python Pandas<\/li><\/ol>\n\n\n\n

        Code: -#Write your code here
        import pandas as pd
        import numpy as np<\/p>\n\n\n\n

        height_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
        height_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

        weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
        weights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

        df_A = pd.DataFrame()
        df_A[‘Student_height’] = height_A
        df_A[‘Student_weight’] = weights_A<\/p>\n\n\n\n

        df_A[‘Gender’] = [‘M’, ‘F’, ‘M’, ‘M’, ‘F’]<\/p>\n\n\n\n

        s = pd.Series([165.4, 82.7, ‘F’], index = [‘Student_height’, ‘Student_weight’, ‘Gender’], name = ‘s6’)<\/p>\n\n\n\n

        df_AA = df_A.append(s)
        print(df_AA)<\/p>\n\n\n\n

        TASK – 2<\/h1>\n\n\n\n

        my_mean = 170.0
        my_std = 25.0
        np.random.seed(100)
        heights_B = pd.Series(np.random.normal(loc = my_mean, scale = my_std, size = 5))
        heights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

        my_mean1 = 75.0
        my_std1 = 12.0
        np.random.seed(100)
        weights_B = pd.Series(np.random.normal(loc = my_mean1, scale = my_std1, size = 5))
        weights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

        df_B = pd.DataFrame()
        df_B[‘Student_height’] = heights_B
        df_B[‘Student_weight’] = weights_B<\/p>\n\n\n\n

        df_B.index = [‘s7’, ‘s8’, ‘s9’, ‘s10’, ‘s11’]
        df_B[‘Gender’] = [‘F’, ‘M’, ‘F’, ‘F’, ‘M’]<\/p>\n\n\n\n

        df = pd.concat([df_AA, df_B])
        print(df)<\/p>\n\n\n\n

        1. Data Merging Hands – On(1) Solutions: -Python Pandas<\/li><\/ol>\n\n\n\n

          Code: -#Write your code here
          import pandas as pd
          import numpy as np
          nameid = pd.Series(range(101, 111))
          name = pd.Series([‘person’ + str(i) for i in range(1, 11)])
          master = pd.DataFrame()
          master[‘nameid’] = nameid
          master[‘name’] = name
          transaction = pd.DataFrame({
          ‘nameid’: [108, 108, 108, 103],
          ‘product’: [‘iPhone’, ‘Nokia’, ‘Micromax’, ‘Vivo’]
          })
          mdf = pd.merge(master, transaction, on = ‘nameid’)
          print(mdf)<\/p>\n\n\n\n

          1. Indexing Dataframe Hands – On Solutions – Python Pandas<\/li><\/ol>\n\n\n\n

            Code: –
            import pandas as pd
            import numpy as np<\/p>\n\n\n\n

            TASK – 1<\/h1>\n\n\n\n

            DatetimeIndex = pd.date_range(start = ’09\/01\/2017′, end = ’09\/15\/2017′)
            print(DatetimeIndex[2])<\/p>\n\n\n\n

            TASK – 2<\/h1>\n\n\n\n

            datelist = [’14-Sep-2017′, ’09-Sep-2017′]
            date_to_be_searched = pd.to_datetime(datelist)
            print(date_to_be_searched)<\/p>\n\n\n\n

            TASK – 3<\/h1>\n\n\n\n

            print(date_to_be_searched.isin(datelist))<\/p>\n\n\n\n

            TASK – 4<\/h1>\n\n\n\n

            arraylist = [
            [‘classA’] * 5 + [‘classB’] * 5, [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’] * 2
            ]
            mi_index = pd.MultiIndex.from_product(arraylist, names = [‘First Level’, ‘Second Level’])
            print(mi_index.levels)<\/p>\n\n\n\n

            1. Data Aggression: -Python Pandas<\/li><\/ol>\n\n\n\n

              Code: -#Write your code here
              import pandas as pd
              import numpy as np
              heights_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
              heights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
              weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
              weights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n

              df_A = pd.DataFrame()<\/p>\n\n\n\n

              df_A[‘Student_height’] = heights_A
              df_A[‘Student_weight’] = weights_A<\/p>\n\n\n\n

              df_A_filter1 = df_A[(df_A.Student_weight < 80.0) & (df_A.Student_height > 160.0)]
              print(df_A_filter1)<\/p>\n\n\n\n

              TASK – 2<\/h1>\n\n\n\n

              df_A_filter2 = df_A[df_A.index.isin([‘s5’])]
              print(df_A_filter2)<\/p>\n\n\n\n

              TASK – 3<\/h1>\n\n\n\n

              df_A[‘Gender’] = [‘M’, ‘F’, ‘M’, ‘M’, ‘F’]
              df_groups = df_A.groupby(‘Gender’)
              print(df_groups.mean())<\/p>\n\n\n\n

              1. Accessing Pandas Data Structures – Python Pandas<\/li><\/ol>\n\n\n\n

                Code: –<\/p>\n\n\n\n

                #Write your code here<\/code><\/pre>\n\n\n\n
                import pandas as pd
                import numpy as np<\/p>\n\n\n\n
                heights_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
                heights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
                print(heights_A[1])<\/p>\n\n\n\n
                TASK 2<\/h1>\n\n\n\n
                print(heights_A[1: 4])<\/p>\n\n\n\n
                TASK 3<\/h1>\n\n\n\n
                weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
                weights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]<\/p>\n\n\n\n
                df_A = pd.DataFrame()
                df_A[‘Student_height’] = heights_A
                df_A[‘Student_weight’] = weights_A<\/p>\n\n\n\n
                height = df_A[‘Student_height’]
                print(type(height))<\/p>\n\n\n\n
                TASK 4<\/h1>\n\n\n\n
                df_s1s2 = df_A[df_A.index.isin([‘s1’, ‘s2’])]
                print(df_s1s2)<\/p>\n\n\n\n
                TASK 5<\/h1>\n\n\n\n
                df_s2s5s1 = df_A[df_A.index.isin([‘s1’, ‘s2’, ‘s5’])]
                df_s2s5s1 = df_s2s5s1.reindex([‘s2’, ‘s5’, ‘s1’])
                print(df_s2s5s1)<\/p>\n\n\n\n
                TASK 6<\/h1>\n\n\n\n
                df_s1s4 = df_A[df_A.index.isin([‘s1’, ‘s4’])]
                print(df_s1s4)<\/p>\n\n\n\n
                1. Working With CSV Files<\/li><\/ol>\n\n\n\n
                  Code: –<\/p>\n\n\n\n
                  #Write your code here<\/code><\/pre>\n\n\n\n
                  import pandas as pd
                  import numpy as np
                  heights_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4])
                  heights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
                  weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
                  weights_A.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
                  df_A = pd.DataFrame()
                  df_A[‘Student_height’] = heights_A
                  df_A[‘Student_weight’] = weights_A
                  df_A.to_csv(‘classA.csv’)<\/p>\n\n\n\n
                  TASK 2<\/h1>\n\n\n\n
                  df_A2 = pd.read_csv(‘classA.csv’)
                  print(df_A2)<\/p>\n\n\n\n
                  TASK 3<\/h1>\n\n\n\n
                  df_A3 = pd.read_csv(‘classA.csv’, index_col = 0)
                  print(df_A3)<\/p>\n\n\n\n
                  TASK 4<\/h1>\n\n\n\n
                  my_mean = 170.0
                  my_std = 25.0
                  np.random.seed(100)
                  heights_B = pd.Series(np.random.normal(loc = my_mean, scale = my_std, size = 5))
                  heights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
                  my_mean1 = 75.0
                  my_std1 = 12.0
                  np.random.seed(100)
                  weights_B = pd.Series(np.random.normal(loc = my_mean1, scale = my_std1, size = 5))
                  weights_B.index = [‘s1’, ‘s2’, ‘s3’, ‘s4’, ‘s5’]
                  df_B = pd.DataFrame()
                  df_B[‘Student_height’] = heights_B
                  df_B[‘Student_weight’] = weights_B<\/p>\n\n\n\n
                  df_B.to_csv(‘classB.csv’, index = False)
                  print(‘classB.csv’)<\/p>\n\n\n\n
                  TASK 5<\/h1>\n\n\n\n
                  df_B2 = pd.read_csv(‘classB.csv’)
                  print(df_B2)<\/p>\n\n\n\n
                  TASK 6<\/h1>\n\n\n\n
                  df_B3 = pd.read_csv(‘classB.csv’, header = None)
                  print(df_B3)<\/p>\n\n\n\n
                  TASK 7<\/h1>\n\n\n\n
                  df_B4 = pd.read_csv(‘classB.csv’, header = None, skiprows = 2)
                  print(df_B4)<\/p>\n","protected":false},"excerpt":{"rendered":"
                  Fresco Play Python Pandas Hands- on Solution || T Factor Fresco Play Python Pandas Hands-on Solution – T Factor (Course ID:- 55937) In Python Pandas(Course Id:- 55937), There are 8 Hands-On Questions Available. The Solutions are\u00a0 Data Structures in Pandas Solution. Code: -#Write your code hereimport pandas as pdimport numpy […]<\/p>\n","protected":false},"author":3,"featured_media":663,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":""},"categories":[156],"tags":[],"_links":{"self":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/723"}],"collection":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/comments?post=723"}],"version-history":[{"count":1,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/723\/revisions"}],"predecessor-version":[{"id":724,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/723\/revisions\/724"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/media\/663"}],"wp:attachment":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/media?parent=723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/categories?post=723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/tags?post=723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}