pandas data cleansing to achieve deletion project practice

Preparation (importing library, importing data)

import pandas as pd
import  as plt
import numpy as np
import seaborn as  sns
sns.set_style("darkgrid")   

list_csv = ['Amazon_top_selling_book.csv','breast_cancer_wisconsin.csv','','','netflix_titles.csv','',
           '','']
dic_path = r'C:\Users\pandas\Desktop\task\228datasets\datasets'
part_data = pd.read_csv(dic_path+'\\'+list_csv[4])
part_data

8807 rows × 12 columns

Status of detection data

Hint: This function is used to detect missing values in any DataFrame.

def missing_values_table(df):
        mis_val = ().sum()
        mis_val_percent = 100 * ().sum() / len(df)
        mis_val_table = ([mis_val, mis_val_percent], axis=1)
        mis_val_table_ren_columns = mis_val_table.rename(
        columns = {0 : 'Missing Values', 1 : '% of Total Values'})
        mis_val_table_ren_columns = mis_val_table_ren_columns[
            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
        '% of Total Values', ascending=False).round(1)
        print ("Your selected dataframe has " + str([1]) + " columns.\n"      
            "There are " + str(mis_val_table_ren_columns.shape[0]) +
              " columns that have missing values.")
        return mis_val_table_ren_columns

missing_values_table(part_data)

Your selected dataframe has 12 columns.
There are 6 columns that have missing values.

(labels=None,axis=0, index=None, columns=None, inplace=False)

Parameter Description:

• labels is the name of the rows and columns to be deleted, given as a list.
• axis defaults to 0 and refers to deleting rows, so specify axis=1 when deleting columns;
• index Directly specifies the row to be deleted
• columns Specify the columns to be deleted directly
• replace=False, by default this delete operation does not change the original data, but returns a new dataframe after performing the delete operation;
• replace=True, then the delete operation will be performed directly on the original data, and cannot be returned after deletion.

Mode 1: Delete specified rows or columns

labels+axis

demo = part_data.drop(['director'], axis=1)
missing_values_table(demo)

Your selected dataframe has 11 columns.
There are 5 columns that have missing values.

Way two: the use of boolean to delete the line that satisfies the condition element

df = (df[].index)

# Delete rows with release_year year before 2009
demo = part_data.drop(part_data[part_data["release_year"]<2009].index)

(7624, 12)

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