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INWe will discuss how to use the like SQL and Not INthe operator to filter pandas in this tutorial DataFrame. In addition, we will also show you how to use isin()the function and 一元运算符(~)filter a single row/column based on a condition, filter multiple columns, filter pandas DataFrame.

Use isin()the function to create a DataFrame from a dictionary object in Pandas

The following example DataFrame contains columns Student Name, Subject, Semester, Marks. Import the pandas library and create a DataFrame.

import pandas as pd

student_record = {
    "Student Name": ["Samreena", "Affan", "Mirha", "Asif"],
    "Subject": ["SDA", "Ethics", "Web Design", "Web Development"],
    "Semester": ["6th", "7th", "5th", "8th"],
    "Marks": [100, 90, 80, 70],
}
index_labels = [0, 1, 2, 3]
df = pd.DataFrame(student_record, index=index_labels)
print(df)

Output:

   Student Name         Subject  Semester  Marks
0     Samreena              SDA      6th    100
1        Affan           Ethics      7th     90
2        Mirha       Web Design      5th     80
3         Asif  Web Development      8th     70

isin()Filtering a Pandas DataFrame using a function

We can filter pandas rows using a method similar to INthe operator in SQL .isin()DataFrame

To filter the rows, a single column is checked for the desired element. Using pd.series.isin()the function, we can check if the search element is present in the series.

If the element would match in the series, return it true, otherwise return it false.

For example, we want to Subjectreturn rows that contain the subjects Web Designand in the column Web Development.

import pandas as pd

student_record = {
    "Name": ["Samreena", "Affan", "Mirha", "Asif"],
    "Subject": ["SDA", "Ethics", "Web Design", "Web Development"],
    "Semester": ["6th", "7th", "5th", "8th"],
    "Marks": [100, 90, 80, 70],
}
index_labels = [0, 1, 2, 3]
dataframe = pd.DataFrame(student_record, index=index_labels)
# Find elements in a Column to return rows
subjects_list = ["Web Design", "Web Development"]
dataframe1 = dataframe[dataframe.Subject.isin(subjects_list)]
print(dataframe1)

Output:

    Name          Subject   Semester  Marks
2   Mirha       Web Design      5th     80
3   Asif   Web Development      8th     70

Note that only those StudentName Web Developmentand Web DesignSubject are returned.

We can return a boolean array by displaying trueand using the Pandas DataFrame row indexing .false

import pandas as pd

student_record = {
    "Name": ["Samreena", "Affan", "Mirha", "Asif"],
    "Subject": ["SDA", "Ethics", "Web Design", "Web Development"],
    "Semester": ["6th", "7th", "5th", "8th"],
    "Marks": [100, 90, 80, 70],
}
index_labels = [0, 1, 2, 3]

dataframe = pd.DataFrame(student_record, index=index_labels)
subjects_list = ["Web Design", "Web Development"]
dataframe1 = dataframe.Subject.isin(subjects_list)
print(dataframe1)

Output:

0    False
1    False
2    True
3    True
Name: Subject, dtype: bool

isin()Filter multiple columns in a Pandas DataFrame using the

We can also isin()apply filters on multiple columns using the method. For example, we want to retrieve all SDArows that have subject or fifth semester.

import pandas as pd

student_record = {
    "Name": ["Samreena", "Affan", "Mirha", "Asif"],
    "Subject": ["SDA", "Ethics", "Web Design", "Web Development"],
    "Semester": ["6th", "7th", "5th", "8th"],
    "Marks": [100, 90, 80, 70],
}
index_labels = [0, 1, 2, 3]
dataframe = pd.DataFrame(student_record, index=index_labels)
dataframe1 = dataframe[
    dataframe[["Subject", "Semester"]].isin(["SDA", "7th"]).any(axis=1)
]
print(dataframe1)

Output:

       Name  Subject  Semester  Marks
0   Samreena    SDA      6th    100
1     Affan  Ethics      7th     90

Filtering a Pandas DataFrame using the method with Not (~)a matching conditionisin()

isin()INThe behavior of the method is similar to the operator in SQL . We will use 一元运算符 (~)to implement Not INthe operator.

For example, we want to display only those rows that do not contain the subject Web Designand .Ethics

import pandas as pd

student_record = {
    "Name": ["Samreena", "Affan", "Mirha", "Asif"],
    "Subject": ["SDA", "Ethics", "Web Design", "Web Development"],
    "Semester": ["6th", "7th", "5th", "8th"],
    "Marks": [100, 90, 80, 70],
}
index_labels = [0, 1, 2, 3]
dataframe = pd.DataFrame(student_record, index=index_labels)
subjects_list = ["Web Design", "Ethics"]

# Applying Not operator
dataframe1 = dataframe[~dataframe.Subject.isin(subjects_list)]
print(dataframe1)

Output:

       Name          Subject  Semester  Marks
0  Samreena              SDA      6th    100
3      Asif  Web Development      8th     70