leetcode - pandas

 

import pandas as pd

def createDataframe(student_data: List[List[int]]) -> pd.DataFrame:

    student_df = pd.DataFrame(student_data , columns=("student_id","age"))

    return student_df;

if __name__ == '__main__':

    student_data = [

        [1, 15],

        [2, 11],

        [3, 11],

        [4, 20]

    ]

    df = createDataframe(student_data) #assign the returned dataframe to df.

    print(df)

import pandas as pd

def getDataframeSize(players: pd.DataFrame) -> List[int]:

  return list(players.shape);

  if '__name__' == __main__:

    df=pd.DataFrame

    rows , columns = getDataframeSize(df)

    print(f"This DataFrame contains {rows} rows and {columns} columns.")

Explanation:

(players.shape)

Here, players is the dataframe

In pandas, the shape The attribute of a DataFrame returns a tuple.

import pandas as pd

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:

    return employees.head(3);

if __name__ == '__main__':

    df=pd.DataFrame

    First_3_rows = selectFirstRows(df)

    print(employees)

answer:

import pandas as pd

def selectData(students: pd.DataFrame):

    student_101 = students.query('student_id == 101')    

    If not student_101.empty:

        return student_101[['name', 'age']]

    else:

        return df

if __name__ =='__main__':

    df= pd.DataFrame

    student=selectData(df)

    print("\nRows name AND age (using .query()):\n", student)

explanation :

To query we have to use df.query()

Rows name AND age (using .query())

This is a descriptive string that explains what the following output represents. It indicates that the output will show the "name" and "age" columns of a DataFrame, and that the data was selected using the .query() method in pandas.

import pandas as pd

def createBonusColumn(employees: pd.DataFrame):

    employees['bonus'] = employees['salary'] * 2

    return employees

if __name__ =='__main__':

    df = pd.DataFrame(data)

    call_df = createBonusColumn(df)

    print(call_df)  

explanation :

We can create the new column in the dataframe by

df[column name] = values

here

df[coloumn name ] = df[col1_values]*2

import pandas as pd

def dropDuplicateEmails(customers: pd.DataFrame):

 non_duplicated_df=customers.drop_duplicates('email')

 return non_duplicated_df

if __name__ == '__main__':

    df=pd.DataFrame

    call_df=dropDuplicateEmails(df)

    print(non_duplicated_df)

explanation :

To remove duplicates, use drop.duplicates()

non_duplicated_df=customers.drop_duplicates('email') --> removing only the duplicates in email coloumn

import pandas as pd

def dropMissingData(students: pd.DataFrame):

    new_df= students.dropna(subset = ['name'])

    return new_df

if __name__ == '__main__':

 df=  pd.DataFrame

 call_df = dropMissingData(df)

 print(new_df)  

dropna() is to drop the missing data in the dataframe

subset(['col_name ']) specifies the particular column

import pandas as pd

def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame:

    employees['salary'] = employees['salary'].apply(lambda salary: salary * 2)

    return employees

if __name__ == '__main__':

    df = pd.DataFrame

    call_df = modifySalaryColumn(df)

    print(call_df)

explanation:

employees['salary'] = employees['salary'].apply(lambda salary: salary * 2)

If we want to modify the column values we have to use lambda functions

import pandas as pd

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:

    renamed_students = students.rename(columns={

        'id': 'student_id',

        'first': 'first_name',

        'last': 'last_name',

        'age': 'age_in_years'

    })

    return renamed_students

if __name__ == '__main__':

    df= pd.DataFrame

    call_df = renameColumns(df)

    print (call_df)

explanation : rename (columns ={values}) to change the name

answer:

import pandas as pd

def changeDatatype(students: pd.DataFrame) -> pd.DataFrame:

   students['grade'] = students['grade'].astype(int)

   return students

if __name__ == '__main__':

    df = pd.DataFrame

    call_df = changeDatatype(df)

explanation :

astype() is used for explicit conversion of the datatype