Pandas Drop Rows With Condition

Use pandas.DataFrame.drop() method to delete/remove rows with condition(s). In my earlier article, I have covered how to drop rows by index from DataFrame, and in this article, I will cover several examples of dropping rows with conditions, for example, string matching on a column value.

Alternatively, you can also achieve dropping rows by filtering rows and assigning them to another DataFrame.

1. Quick Examples of Drop Rows With Condition in Pandas

If you are in a hurry, below are some quick examples of pandas dropping/removing/deleting rows with condition(s).


# 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 the DataFrame rows. 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.


   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. Using DataFrame.drop() to Drop Rows with Condition

drop() method takes several params that help you to delete rows from DataFrame by checking condition. When condition expression satisfies it returns True which actually removes the rows.


df.drop(df[df['Fee'] >= 24000].index, inplace = True)
print(df)

Yields below 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[] to Drop Rows by Condition

Alternatively, you can also try another most used approach to drop rows by condition 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.


   Courses      Fee Duration  Discount
1  PySpark  25000.0     None    2300.0
3   Python  24000.0      NaN       NaN

3. Drop Rows Based on Multiple Conditions

Sometimes it may require you to drop the rows based on multiple conditions. You can just extend the usage of the above examples to do so.


# Delect rows based on multiple column value
df = pd.DataFrame(technologies)
df = df[ (df['Fee'] >= 22000) & (df['Discount'] == 2300)]
print(df)

Yields below output.


   Courses      Fee Duration  Discount
1  PySpark  25000.0     None    2300.0

4. Other Ways to Delete Rows from pandas DataFrame

You can also delete 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()

5. Delete Rows Based on Inverse of Condition

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.


   Courses    Fee Duration  Discount
b  PySpark  25000   50days      2300
f  PySpark  25000   50days      2000

6. Complete Example

Below is a complete example of how to remove/delete/drop rows with conditions in 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)

# 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 drop/delete/remove pandas DataFrame rows with single and multiple conditions by using examples.

Happy Learning !!

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