Drop / Keep rows based on column values using column location instaed of column names.

data = {"product_name":["Keyboard","Mouse", "Monitor", "CPU","CPU", "Speakers"],
        "Unit_Price":[500,200, 5000.235, 10000.550, 10000.550, 250.50],
        "No_Of_Units":[5,5, 10, 20, 20, 8],
        "Available_Quantity":[5,6,10,1,3,2],
        "Available_Since_Date":['11/5/2021', '4/23/2021', '08/21/2021','09/18/2021','09/18/2021','01/05/2021']
       }
   

Drop / Keep rows with 'CPU' in column 'product_name' but without using column name.

CodePudding user response：

Use iloc for selection of the column by position, then boolean indexing:

# select the first column and check if the items contain "CPU"
m = df.iloc[:, 0].str.contains('CPU')
# for strict equality
# m = df.iloc[:, 0].eq('CPU')


# keep CPU
df_keep = df[m]

# drop CPU
df_drop = df[~m]

Output:

# df_keep
  product_name  Unit_Price  No_Of_Units  Available_Quantity Available_Since_Date
3          CPU    10000.55           20                   1           09/18/2021
4          CPU    10000.55           20                   3           09/18/2021

# df_drop
  product_name  Unit_Price  No_Of_Units  Available_Quantity Available_Since_Date
0     Keyboard     500.000            5                   5            11/5/2021
1        Mouse     200.000            5                   6            4/23/2021
2      Monitor    5000.235           10                  10           08/21/2021
5     Speakers     250.500            8                   2           01/05/2021