modifying global variable pass in parameter function in place on python

Question

i thought i understood the global vs local scope for variable but i am struggling on a case.

Here the function i want to implement:

def login_table(id_name_verified, id_password):
    """
    :param id_name_verified: (DataFrame) DataFrame with columns: Id, Login, Verified.
    :param id_password: (numpy.array) Two-dimensional NumPy array where each element
                        is an array that contains: Id and Password
    :returns: (None) The function should modify id_name_verified DataFrame in-place.
              It should not return anything.
     Goal : in id_name_verified, 'Verified' column should be removed and password should be added from id_password with corresponding id and the column be named password
    """


Test:
import pandas as pd
import numpy as np


def login_table(id_name_verified, id_password):
    id_name_verified.drop(columns="Verified",inplace=True)
    password = pd.DataFrame(id_password)
    password.columns = ["Id", "Password"]
    id_name_verified =id_name_verified.merge(password, on=['Id'])




id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

Expected Output:

   Id        Login   Password
0   1      JohnDoe  987340123
1   2  AnnFranklin  187031122

Output i got:

   Id        Login
0   1      JohnDoe
1   2  AnnFranklin

When I run this on pycharm, i see the problem is in the last line of my function where id_name_verified is identified as being from outer scope. This inspection detects shadowing names defined in outer scopes.

If i don't define a function it will work so I guess there is something I miss in the understanding of parameters pass to a function; any suggestions ?

This is not a global vs local variables problem (apart from pycharm warning about shadowing). login_table() should return id_name_verified and the call site should be: id_name_verified = login_table(id_name_verified, id_password) — quamrana
– quamrana, Commented Nov 12, 2019 at 15:54
i get what you mean but if i want to satisfy the specs of the function, i don't want the function to return something, so modify it in place as i said, and neither change the number of parameter — Rona
– Rona, Commented Nov 12, 2019 at 16:21

Sushanth · Accepted Answer · 2019-11-12 16:01:19Z

1

Here is the code that will solve your problem:

import pandas as pd
import numpy as np

def login_table( id_password):
    global id_name_verified
    id_name_verified.drop(columns="Verified",inplace=True)
    password = pd.DataFrame(id_password)
    password.columns = ["Id", "Password"]
    id_name_verified =id_name_verified.merge(password, on=['Id'])

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table( id_password)
print(id_name_verified)

The changes I made are, I declared the "id_name_verified" as global inside the function "login_table". Once this is done you don't have to pass "id_name_verified" again to the function because by using the keyword "global" we have already given access of "id_name_verified" to the function "login_table". So removed the "id_name_verified" as one of the arguments to the function.

edited Nov 12, 2019 at 16:01

answered Nov 12, 2019 at 15:56

Sushanth

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1 Comment

quamrana Over a year ago

As a programmer you should try very, very, very hard not to use global variables!

RamWill · Accepted Answer · 2021-03-20 16:44:01Z

import pandas as pd
import numpy as np

def login_table(id_name_verified, id_password):
    id_name_verified.drop(inplace=True, columns="Verified") # drop verified column inplace
    id_name_verified.insert(loc=2,column="Password",value=np.nan) # insert password column inplace
    for row in range(len(id_password)): 
        # apply the password on the rows that match using at
        id = id_password[row][0]
        pw = id_password[row][1]
        id_name_verified.at[row, "Password"] = pw

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

Tokay · Accepted Answer · 2022-07-13 23:38:12Z

For faster version use mapping. Creating new DataFrame is slow and costly way.

import pandas as pd
import numpy as np

def login_table(id_name_verified, id_password):
    """
    :param id_name_verified: (DataFrame) DataFrame with columns: Id, Login, Verified.   
    :param id_password: (numpy.array) Two-dimensional NumPy array where each element
                        is an array that contains: Id and Password
    :returns: (None) The function should modify id_name_verified DataFrame in-place. 
              It should not return anything.
    """   
    id_name_verified.drop(columns='Verified', inplace=True)
    mapping = {li[0]: li[1] for li in id_password}
    id_name_verified['Password'] = id_name_verified['Id'].map(mapping)

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

python_enthusiast · Accepted Answer · 2020-03-04 12:20:01Z

Here's the code that passes all 4 tests on TestDome:

import pandas as pd
import numpy as np

def login_table(id_name_verified, id_password):
    """
    :param id_name_verified: (DataFrame) DataFrame with columns: Id, Login, Verified.   
    :param id_password: (numpy.array) Two-dimensional NumPy array where each element
                        is an array that contains: Id and Password
    :returns: (None) The function should modify id_name_verified DataFrame in-place. 
              It should not return anything.
    """   
    id_name_verified.drop(columns = 'Verified', inplace = True)
    password_df = pd.DataFrame(id_password)
    pass_col = password_df[1]
    id_name_verified['Password'] = pass_col

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

You drop the 'Verified' column, then create a new data frame just out of id_password, take the column that contains passwords and add it to the id_name_verified data frame.

Abbos-Ali Mirkhanov · Accepted Answer · 2020-06-26 01:42:20Z

0

import pandas as pd
import numpy as np 

def login_table(id_name_verified, id_password):
    # in place makes sure that changes are made in a current dataframe 
    id_name_verified.drop('Verified', 1, inplace= True) 
    # id_password[:,1] -> : selects all rows and 1 selects only second column
    id_name_verified['Password'] = id_password[:,1]
    pass


id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

edited Jun 26, 2020 at 1:42

answered Jun 26, 2020 at 1:37

Abbos-Ali Mirkhanov

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