Use drop() method to delete rows based on column value in pandas DataFrame, as part of the data cleansing, you would be required to drop rows from the DataFrame when a column value matches with a static value or on another column value.
In my earlier article, I have covered how to drop rows by index label from DataFrame, and in this article, I will cover several examples of dropping rows based on column value.
Alternatively, you can also achieve dropping rows by filtering rows and assigning them to another DataFrame.
1. Quick Examples of Delete Pandas Rows Based on Column Value
If you are in a hurry, below are some quick examples of pandas deleting rows based on column value.
# Quick Examples
# Using drop() to delete rows based on column value
df.drop(df[df['Fee'] >= 24000].index, inplace = True)
# Remove rows
df2 = df[df.Fee >= 24000]
# If you have space in column name
# Specify column name with in single quotes
df2 = df[df['column name']]
# Using loc
df2 = df.loc[df["Fee"] >= 24000 ]
# Delect rows based on multiple column value
df2 = df[ (df['Fee'] >= 22000) & (df['Discount'] == 2300)]
# Drop rows with None/NaN
df2 = df[df.Discount.notnull()]
Let’s create a DataFrame with a few rows and columns and execute some examples to learn how to drop DataFrame row. Our DataFrame contains column names Courses, Fee, Duration, and Discount.
# Create pandas DataFrame
import pandas as pd
import numpy as np
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python"],
'Fee' :[22000,25000,np.nan,24000],
'Duration':['30day',None,'55days',np.nan],
'Discount':[1000,2300,1000,np.nan]
}
df = pd.DataFrame(technologies)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
0 Spark 22000.0 30day 1000.0
1 PySpark 25000.0 None 2300.0
2 Hadoop NaN 55days 1000.0
3 Python 24000.0 NaN NaN
2. Delete Rows Based on Column Values
drop() method takes several params that help you to delete rows from DataFrame by checking column values. When the expression is satisfied it returns True which actually removes the rows.
# Delete rows using drop()
df.drop(df[df['Fee'] >= 24000].index, inplace = True)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
0 Spark 22000.0 30day 1000.0
2 Hadoop NaN 55days 1000.0
After removing rows, it is always recommended to reset the row index.
2. Using loc[]
Alternatively, you can also try another most used approach to drop rows based on column values using loc[] and df[].
Note that these methods actually filter the data, by negating this you will get the desired output.
# Remove row
df2 = df[df.Fee >= 24000]
print(df2)
# Using loc[]
df2 = df.loc[df["Fee"] >= 24000 ]
print(df2)
Yields same output as above.
# Output:
Courses Fee Duration Discount
1 PySpark 25000.0 None 2300.0
3 Python 24000.0 NaN NaN
3. Delete Rows Based on Multiple Column Values
Sometimes it may require you to delete the rows based on matching values of multiple columns.
# Delect rows based on multiple column value
df = pd.DataFrame(technologies)
df = df[ (df['Fee'] >= 22000) & (df['Discount'] == 2300)]
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
1 PySpark 25000.0 None 2300.0
4. Delete Rows Based on None or NaN Column Values
When you have None or NaN values on columns, you may need to remove these before you apply some calculations. you can do this using notnull() function.
Note: With None or NaN values you cannot use == or != operators.
# Drop rows with None/NaN values
df2 = df[df.Discount.notnull()]
print(df2)
Yields below output
# Output:
Courses Fee Duration Discount
0 Spark 22000.0 30day 1000.0
1 PySpark 25000.0 None 2300.0
2 Hadoop NaN 55days 1000.0
5. using query()
You can also remove rows by using query() method. Note that these methods actually filter the rows from pandas DataFrame, by negating this you can drop the rows.
# Delete rows using DataFrame.query()
df2=df.query("Courses == 'Spark'")
# Using variable
value='Spark'
df2=df.query("Courses == @value")
# Inpace
df.query("Courses == 'Spark'",inplace=True)
# Not equals, in & multiple conditions
df.query("Courses != 'Spark'")
df.query("Courses in ('Spark','PySpark')")
df.query("`Courses Fee` >= 23000")
df.query("`Courses Fee` >= 23000 and `Courses Fee` <= 24000")
# Other ways to Delete Rows
df.loc[df['Courses'] == value]
df.loc[df['Courses'] != 'Spark']
df.loc[df['Courses'].isin(values)]
df.loc[~df['Courses'].isin(values)]
df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]
df.loc[(df['Discount'] >= 1200) & (df['Fee'] >= 23000 )]
df[df["Courses"] == 'Spark']
df[df['Courses'].str.contains("Spark")]
df[df['Courses'].str.lower().str.contains("spark")]
df[df['Courses'].str.startswith("P")]
# Using lambda
df.apply(lambda row: row[df['Courses'].isin(['Spark','PySpark'])])
df.dropna()
6. Based on Inverse of Column Values
If you need to drop() all rows which are not equal to a value given for a column. pandas offer negation (~) operation to perform this feature. For E.x: df.drop(df1,inplace=True).
# Delect rows based on inverse of column values
df1 = df[~(df['Courses'] == "PySpark")].index
df.drop(df1, inplace = True)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
b PySpark 25000 50days 2300
f PySpark 25000 50days 2000
7. Complete Example
import pandas as pd
import numpy as np
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python"],
'Fee' :[22000,25000,np.nan,24000],
'Duration':['30day',None,'55days',np.nan],
'Discount':[1000,2300,1000,np.nan]
}
df = pd.DataFrame(technologies)
print(df)
# Using drop() to remove rows
df.drop(df[df['Fee'] >= 24000].index, inplace = True)
print(df)
# Remove rows
df = pd.DataFrame(technologies)
df2 = df[df.Fee >= 24000]
print(df2)
# Reset index after deleting rows
df2 = df[df.Fee >= 24000].reset_index()
print(df2)
# If you have space in column name.
# Surround the column name with single quote
df2 = df[df['column name']]
# Using loc
df2 = df.loc[df["Fee"] >= 24000 ]
print(df2)
# Delect rows based on multiple column value
df2 = df[(df['Fee'] >= 22000) & (df['Discount'] == 2300)]
print(df2)
# Drop rows with None/NaN
df2 = df[df.Discount.notnull()]
print(df2)
Conclusion
In this article, you have learned how to delete DataFrame rows based on column value using different ways
Happy Learning !!
Related Articles
- How to Drop Rows From Pandas DataFrame Examples
- Drop Single & Multiple Columns From Pandas DataFrame
- Get the Row Count From Pandas DataFrame
- Change Column Data Type On Pandas DataFrame
- Pandas apply() Function to Single & Multiple Column(s)
- Pandas Add Multiple Columns to DataFrame
- Pandas Filter DataFrame by Multiple Conditions
- Apply Multiple Filters to Pandas DataFrame or Series
- Pandas Drop First Column From DataFrame
