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We will look at methods to delete rows based on by using .drop(with and without loc) and 布尔掩码conditions checking column values .DataFrame

Deleting rows of column values ​​in .dropPandas usingDataFrame

.dropThe method accepts one or a list of column names and removes the rows or columns. For rows, we set the parameter axis=0and for columns, we set the parameter ( by axis=1default, ). We can also get the and series column values, based on the condition applied in Pandas .axis0TrueFalseDataFrame

Sample code:

# python 3.x
import pandas as pd

fruit_list = [
    ("Orange", 34, "Yes"),
    ("Mango", 24, "No"),
    ("banana", 14, "No"),
    ("Apple", 44, "Yes"),
    ("Pineapple", 64, "No"),
    ("Kiwi", 84, "Yes"),
]

# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
# Get names of indexes for which column Stock has value No
indexNames = df[df["Stock"] == "No"].index
# Delete these row indexes from dataFrame
df.drop(indexNames, inplace=True)
print(df)

Output:

     Name  Price Stock
0  Orange     34   Yes
3   Apple     44   Yes
5    Kiwi     84   Yes

We can also achieve similar results by df.dropusing in the method ..loc

df.drop(df.loc[df["Stock"] == "Yes"].index, inplace=True)

We can also delete rows based on multiple column values. In the above example, we can delete the rows with price >=30and price <=70.

Sample code:

# python 3.x
import pandas as pd

# List of Tuples
fruit_list = [
    ("Orange", 34, "Yes"),
    ("Mango", 24, "No"),
    ("banana", 14, "No"),
    ("Apple", 44, "Yes"),
    ("Pineapple", 64, "No"),
    ("Kiwi", 84, "Yes"),
]

# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
indexNames = df[(df["Price"] >= 30) & (df["Price"] <= 70)].index
df.drop(indexNames, inplace=True)
print(df)

Output:

     Name  Price Stock
1   Mango     24    No
2  banana     14    No
5    Kiwi     84   Yes

The rows where the price is greater than 30 and less than 70 have been deleted.

Boolean Masking Method to Delete Rows in Pandas DataFrame

Boolean masking is the best and simplest way boolean maskingto delete rows in Pandas based on column values .DataFrame

Sample code:

# python 3.x
import pandas as pd

# List of Tuples
fruit_list = [
    ("Orange", 34, "Yes"),
    ("Mango", 24, "No"),
    ("banana", 14, "No"),
    ("Apple", 44, "Yes"),
    ("Pineapple", 64, "No"),
    ("Kiwi", 84, "Yes"),
]

# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
print(df[df.Price > 40])
print("............................")
print(df[(df.Price > 40) & (df.Stock == "Yes")])

Output:

        Name  Price Stock
3      Apple     44   Yes
4  Pineapple     64    No
5       Kiwi     84   Yes
............................
    Name  Price Stock
3  Apple     44   Yes
5   Kiwi     84   Yes