Page 80 - Informatics_Practices_Fliipbook_Class12
P. 80

Series method unique() returns the distinct elements in a column. The unique values are returned in the
          Ø
              order in which they appear in the original DataFrame column and the missing values (NaN) are ignored in the
              output.

              The method value_counts() returns the count of occurrences of each unique value in a column of the
          Ø
              dataframe.
              Pandas method pd.concat() allow us to concatenate a DataFrame with the another DataFrame.
          Ø
              Pandas method drop() can be used to drop a column or row from the DataFrame.
          Ø
              The rename() method in Pandas is used to rename columns. It allows us to specify new names for one
          Ø
              or more columns using a dictionary. The syntax for using the rename() method to rename columns is as
              follows:
              df.rename(columns={'current_name': 'new_name'}, inplace=True)
              We may also rename columns in a Pandas DataFrame by specifying the complete list of names of the columns,
          Ø
              retaining some of the column names as it is while modifying some others by assigning it to attribute columns
              of groceryDF.
              We can save the DataFrame object as CSV (Comma-Separated Values) file using the to_csv() function of
          Ø
              Pandas DataFrame:

              df.to_csv('output.csv', index=False)
              We  can  organise  the  data  in  a  DataFrame into groups  using the function  groupby(),  based  on
          Ø
              specific criteria, such as values stored in one or more columns. The method returns a DataFrameGroupBy
              object.
              We can apply more than one aggregate operations by passing the list of operations to be carried out on the
          Ø
              groups of data as an argument to the function agg.




                                               Solved Exercise


        A.  Multiple Choice Questions
           1.  Which of the following methods can be used to create a Pandas DataFrame from the following dictionary:
              details = {'Name': ['John', 'Alice', 'Bob'], 'Age': [25, 30, 35]}?
              a.  df = pd.DataFrame(details)
              b.  df = pd.DataFrame.from_dict(details)
              c.  df = pd.DataFrame.from_dictlist(details)
              d.  df = pd.DataFrame.from_series(details)
           2.  Consider the following Pandas Series:
              s = pd.Series([10, 20, 30, 40])
              Which of the following method can be used to create a DataFrame with the series as a column named 'Prices'?

              a.  df = pd.DataFrame({'Prices': s})
              b.  df = pd.DataFrame(s, columns=['Prices'])
              c.  df = pd.DataFrame.from_series({'Prices': s})
              d.  df = pd.DataFrame.from_dict({'Prices': s})
           3.  Which of the following is the correct way to read a CSV file in the Pandas DataFrame?
              a.  df = pd.read_csv('details.csv')             b. df = pd.load_csv('details.csv')
              c.  df = pd.create_dataframe('details.csv') d. df = pd.read_data('details.csv')

          66   Touchpad Informatics Practices-XII
   75   76   77   78   79   80   81   82   83   84   85