Filtering the DataFrame
Filtering a pandas
DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the []
operator with conditions inside, such as df[df['column'] > value]
, or the .query()
method, like df.query('column > value')
.
For example, suppose you have a DataFrame df with columns "Name"
, "Age"
, and "Gender"
, and you want to select all rows where the values of the "Age"
column are greater than 30. You can use the following code to filter the DataFrame:
# Filter the DataFrame using
# the [] operator
df_filtered = df[df["Age"] > 30]
# Filter the DataFrame using
# the .query() method
df_filtered = df.query("Age > 30")
You can also use the "&"
(logical and) and "|"
(logical or) operators to combine multiple conditions.
Swipe to start coding
- Filter the
data
DataFrame using multiple conditions (use the logical and operator to combine them):- The value of the
'MANAGER_ID'
column is greater than 100. - The value of the
'LOCATION_ID'
column is equal to 1700.
- The value of the
Lösung
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Filtering the DataFrame
Filtering a pandas
DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the []
operator with conditions inside, such as df[df['column'] > value]
, or the .query()
method, like df.query('column > value')
.
For example, suppose you have a DataFrame df with columns "Name"
, "Age"
, and "Gender"
, and you want to select all rows where the values of the "Age"
column are greater than 30. You can use the following code to filter the DataFrame:
# Filter the DataFrame using
# the [] operator
df_filtered = df[df["Age"] > 30]
# Filter the DataFrame using
# the .query() method
df_filtered = df.query("Age > 30")
You can also use the "&"
(logical and) and "|"
(logical or) operators to combine multiple conditions.
Swipe to start coding
- Filter the
data
DataFrame using multiple conditions (use the logical and operator to combine them):- The value of the
'MANAGER_ID'
column is greater than 100. - The value of the
'LOCATION_ID'
column is equal to 1700.
- The value of the
Lösung
Danke für Ihr Feedback!