I have 2 dataframes containing names and some demographic information, the dataframes are not identical due to monthly changes.
I'd like to create another df to show just the names of people where there are changes in either their COUNTRY or JOBCODE or MANAGERNAME columns, and also show what kind of changes these are.
Have tried the following code so far and am able to detect changes in the country column in the 2 dataframes for the common rows.
But am not so sure how to capture the movement in the MOVEMENT columns. Appreciate any form of help.
#Merge first
dfmerge = pd.merge(df1, df2, how ='inner', on ='EMAIL')
#create function to get COUNTRY_CHANGE column
def change_in(dfmerge):
if dfmerge['COUNTRY_x'] != dfmerge['COUNTRY_y']:
return 'YES'
else:
return 'NO'
dfmerge['COUNTRYCHANGE'] = dfmerge.apply(change_in, axis = 1)
Dataframe 1
NAME EMAIL COUNTRY JOBCODE MANAGERNAME
Jason Kelly jasonkelly#123.com USA 1221 Jon Gilman
Jon Gilman jongilman#123.com CANADA 1222 Cindy Lee
Jessica Lang jessicalang#123.com AUSTRALIA 1221 Esther Donato
Bob Wilder bobwilder#123.com ROMANIA 1355 Mike Lens
Samir Bala samirbala#123.com CANADA 1221 Ricky Easton
Dataframe 2
NAME EMAIL COUNTRY JOBCODE MANAGERNAME
Jason Kelly jasonkelly#123.com VIETNAM 1221 Jon Gilman
Jon Gilman jongilman#123.com CANADA 4464 Sheldon Tracey
Jessica Lang jessicalang#123.com AUSTRALIA 2224 Esther Donato
Bob Wilder bobwilder#123.com ROMANIA 1355 Emilia Tanner
Desired Output
EMAIL COUNTRY_CHANGE COUNTRY_MOVEMENT JOBCODE_CHANGE JOBCODE_MOVEMENT MGR_CHANGE MGR_MOVEMENT
jasonkelly#123.com YES FROM USA TO VIETNAM NO NO NO NO
jongilman#123.com NO NO YES FROM 1222 to 4464 YES FROM Cindy Lee to Sheldon Tracey
jessicalang#123.com NO NO YES FROM 1221 to 2224 NO NO
bobwilder#123.com NO NO NO NO YES FROM Mike Lens to Emilia Tanner
There is not direct feature in pandas that can help but we may leverage merge function as follows. We are merging dataframes and providing suffix to merged columns and then reporting their differences via this code.
# Assuming df1 and df2 are input data frames in your example.
df3 = pd.merge(df1, df2, on=['name', 'email'], suffixes=['past', 'present'])
dfans = pd.DataFrame() # this is the final output data frame
for column in df1.columns:
if not (column + 'present' in df3.columns or column + 'past' in df3.columns):
# Here we handle those columns which will not be merged like name and email.
dfans.loc[:, column] = df1.loc[:, column] # filling name and email as it is
else:
# string manipulation to name columns correctly in output
newColumn1 = '{}_CHANGE'.format(column)
newColumn2 = '{}_MOVEMENT'.format(column)
past, present = "{}past".format(column), "{}present".format(column)
# creating the output based on input
dfans.loc[:, newColumn1] = (df3[past] == df3[present]).map(lambda x: "YES" if x != 1 else "NO")
dfans.loc[:, newColumn2] = ["FROM {} TO {}".format(x, y) if x != y else "NO" for x, y in
zip(df3[past], df3[present])]
Related
I have a dataframe containing a pandas series (column 2) as below:
column 1
column 2
column 3
1123
Requested By = John Doe 1\n Requested On = 12 October 2021\n Comments = This is a generic request
INC29192
1251
NaN
INC18217
1918
Requested By = John Doe 2\n Requested On = 2 September 2021\n Comments = This is another generic request
INC19281
I'm struggling to extract, split and map column 2 data to a series of new column names with the appropriate data for that record (where possible, that is where there is data available as I have NaNs).
The Desired output is something like (where Ive dropped the column 3 data for legibility):
column 1
column 3
Requested By
Requested On
Comments
1123
INC29192
John Doe 1
12 October 2021
This is a generic request
1251
INC18217
NaN
NaN
NaN
1918
INC19281
John Doe 2
2 September 2021
This is another generic request
I have spent quite some time, trying various approaches, from lambda functions to comprehensions to explode methods but havent quite found a solution that provides the desired output.
First I would convert column 2 values to dictionaries and then convert them to Dataframes and join them to your df:
df['column 2'] = df['column 2'].apply(lambda x:
{y.split(' = ',1)[0]:y.split(' = ',1)[1]
for y in x.split(r'\n ')}
if not pd.isna(x) else {})
df = df.join(pd.DataFrame(df['column 2'].values.tolist())).drop('column 2', axis=1)
print(df)
Output:
column 1 column 3 Requested By Requested On Comments
0 1123 INC29192 John Doe 1 12 October 2021 This is a generic request
1 1251 INC18217 NaN NaN NaN
2 1918 INC19281 John Doe 2 2 September 2021 This is another generic request
Let's say I have dataframes that looks like this:
df_one
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
3 Ian Org Paid
df_two:
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
The logic I am trying to implement is something like this:
If all of column C = "NaN" then drop the entire column
Else if all of column C = "Member" drop the entire column
else do nothing
Any suggestions?
Edit: Added Expected Output
Expected Output if using on both data frames:
df_one
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
3 Ian Org Paid
df_two:
a b
0 dave blue
1 bill red
2 sally green
Edit #2: Why am I doing this in the first place?
I am ripping text from a PDF file into placing into CSV files using the Tabula library.
The data is not coming out in the way that I am hoping it would, so I am applying ETL concepts to move the data around.
The final outcome would be for management to be able to open the final result into a nicely formatted Excel file.
Some of the columns have part of the headers put into a separate row and things got shifted around for some reason while ripping the data out of the PDF.
The headers look something like this:
Team Type Member Contact
Count
What I am doing is checking an entire column for certain header values. If the entire column has a header value, I'm dropping the entire column.
Idea is replace Member to missing values first, then test if at least one no missing value by notna with any and add all columns with Trues for mask by Series.reindex:
mask = (df[['c']].replace('Member',np.nan)
.notna()
.any()
.reindex(df.columns, axis=1, fill_value=True))
print (mask)
Another idea id chain both mask by & for bitwise AND:
mask = ((df[['c']].notna() & df[['c']].ne('Member'))
.any()
.reindex(df.columns, axis=1, fill_value=True))
print (mask)
Last filter by columns in DataFrame.loc:
df = df.loc[:, mask]
Here's an alternate approach to do this.
import pandas as pd
import numpy as np
c = ['a','b','c']
d = [
['dave', 'blue', np.NaN],
['bill', 'red', np.NaN],
['sally', 'green', 'Member'],
['Ian', 'Org', 'Paid']]
df1 = pd.DataFrame(d,columns = c)
df2 = df1.loc[df1['a'] != 'Ian']
print (df1)
print (df2)
if df1.c.replace('Member',np.NaN).isnull().all():
df1 = df1[df1.columns.drop(['c'])]
print (df1)
if df2.c.replace('Member',np.NaN).isnull().all():
df2 = df2[df2.columns.drop(['c'])]
print (df2)
Output of this is:
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
3 Ian Org Paid
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
a b c
0 dave blue NaN
1 bill red NaN
2 sally green Member
3 Ian Org Paid
a b
0 dave blue
1 bill red
2 sally green
my idea is simple, maybe it will help you. I wanna make sure that you want this one: drop the whole column if this column contains only NaN or 'Member' else do nothing.
So I need to check the column first (contain only NaN or 'Member'). We change 'Member' to NaN and use numpy for a test(or something else).
import pandas as pd
df = pd.DataFrame({'A':['dave','bill','sally','ian'],'B':['blue','red','green','org'],'C':[np.nan,np.nan,'Member','Paid']})
df2 = df.drop(index=[3],axis=0)
print(df)
print(df2)
# df 1
col = pd.Series([np.nan if x=='Member' else x for x in df['C'].tolist()])
if col.isnull().all():
df = df.drop(columns='C')
# df2
col = pd.Series([np.nan if x=='Member' else x for x in df2['C'].tolist()])
if col.isnull().all():
df2 = df2.drop(columns='C')
print(df)
print(df2)
A B C
0 dave blue NaN
1 bill red NaN
2 sally green Member
3 ian org Paid
A B
0 dave blue
1 bill red
2 sally green
I have a column called users in dataframe which doesn't have a unique format. I am doing a data cleanup project as the data looks unreadable.
company Users
A [{"Name":"Martin","Email":"name_1#email.com","EmpType":"Full"},{"Name":"Rick","Email":"name_2#email.com","Dept":"HR"}]
B [{"Name":"John","Email":"name_2#email.com","EmpType":"Full","Dept":"Sales" }]
I used the below query to this has broke down the data frame as below
df2 = df
df2 = df2.join(df['Users_config'].str.split('},{', expand=True).add_prefix('Users'))
company Users0 Users1
A "Name":"Martin","Email":"name_1#email.com","EmpType":"Full" "Name":"Rick","Email":"name_2#email.com","Dept":"HR"
B "Name":"John","Email":"name_2#email.com","EmpType":"Full","Dept":"Sales"
and further breaking the above df with "," using the same query I got the output as
Company Users01 Users02 Users03 Users10 Users11 Users12
1 "Name":"Martin" "Email":"name_1#email.com" "EmpType":"Full" "Name":"Rick" "Email":"name_2#email.com" "Dept":"HR"
2 "Name":"John" "Email":"name_2#email.com" "EmpType":"Full" "Dept":"Sales"
As this dataframe looks messy I want to get the output as below. I feel the best way to name the column is to use the column value "Name" from "Name":"Martin" itself and If we hardcore using df.rename the column name will get mismatch.
Company Name_1 Email_1 EmpType_1 Dept_1 Name_2 Email_2 Dept_2
1 Martin name_1#email.com Full Rick name_2#email.com "HR"
2 John name_2#email.com" Full Sales
Is there any way I can get the above output from the original dataframe.
Use:
df['Users'] = df['Users'].apply(ast.literal_eval)
d = df.explode('Users').reset_index(drop=True)
d = d.join(pd.DataFrame(d.pop('Users').tolist()))
d = d.set_index(['company', d.groupby('company').cumcount().add(1).astype(str)]).unstack()
d.columns = d.columns.map('_'.join)
Details:
First we use ast.literal_eval to evaluate the strings in Users column, then use DataFrame.explode on column Users to create a dataframe d.
print(d)
company Users
0 A {'Name': 'Martin', 'Email': 'name_1#email.com', 'EmpType': 'Full'}
1 A {'Name': 'Rick', 'Email': 'name_2#email.com', 'Dept': 'HR'}
2 B {'Name': 'John', 'Email': 'name_2#email.com', 'EmpType': 'Full', 'Dept': 'Sales'}
Create a new dataframe from the Users column in d and use DataFrame.join to join this new dataframe with d.
print(d)
company Name Email EmpType Dept
0 A Martin name_1#email.com Full NaN
1 A Rick name_2#email.com NaN HR
2 B John name_2#email.com Full Sales
Use DataFrame.groupby on column company then use groupby.cumcount to create a counter for each group, then use DataFrame.set_index to set the index of d as company + counter. Then use DataFrame.unstack to reshape the dataframe creating MultiIndex columns.
print(d)
Name Email EmpType Dept
1 2 1 2 1 2 1 2
company
A Martin Rick name_1#email.com name_2#email.com Full NaN NaN HR
B John NaN name_2#email.com NaN Full NaN Sales NaN
Finally use map along with .join to flatten the MultiIndex columns.
print(d)
Name_1 Name_2 Email_1 Email_2 EmpType_1 EmpType_2 Dept_1 Dept_2
company
A Martin Rick name_1#email.com name_2#email.com Full NaN NaN HR
B John NaN name_2#email.com NaN Full NaN Sales NaN
I have a DataFrame that looks like this:
name birth
John Henry Smith 1980
Hannah Gonzalez 1900
Michael Thomas Ford 1950
Michelle Lee 1984
And I want to create two new columns, "middle" and "last" for the middle and last names of each person, respectively. People who have no middle name should have None in that data frame.
This would be my ideal result:
name middle last birth
John Henry Smith 1980
Hannah None Gonzalez 1900
Michael Thomas Ford 1950
Michelle None Lee 1984
I have tried different approaches, such as this:
df['middle'] = df['name'].map(lambda x: x.split(" ")[1] if x.count(" ")== 2 else None)
df['last'] = df['name'].map(lambda x: x.split(" ")[1] if x.count(" ")== 1 else x.split(" ")[2])
I even made some functions that try to do the same thing more carefully, but I always get the same error: "List Index out of range". This is weird because if I go about printing df.iloc[i,0].split(" ") for i in range(len(df)), I do get lists with length 2 or length 3 only.
I also printed x.count(" ") for all x in the "name" column and I always got either 1 or 2 as a result. There are no single names.
This is my first question so thank you so much!
Use Series.str.replace with expand = True.
df2 = (df['name'].str
.split(' ',expand = True)
.rename(columns = {0:'name',1:'middle',2:'last'}))
new_df = df2.assign(middle = df2['middle'].where(df2['last'].notnull()),
last = df2['last'].fillna(df2['middle']),
birth = df['birth'])
print(new_df)
name middle last birth
0 John Henry Smith 1980
1 Hannah NaN Gonzalez 1900
2 Michael Thomas Ford 1950
3 Michelle NaN Lee 1984
I have a column name called Student name and each row has four or five student names -- like this John mills, Tim Harry, Alex win, Kate marry... I want to take the first two student names and store into a new column called Student 1 and Student 2. Names have been separated from comma.
I created a function and i can able to extract first student name . result storing into my dataframe called student_0
def find_student(df2):
for i in range(2):
df2[f"student name_{i}"] = [x.split(',')[i] for x in df2["student name"]]
return df2
new_df = find_student(df2)
df2 is my dataframe name
I AM NOT GETTING SECOND STUDENT NAME. PLEASE ADVISE
Use Series.str.split with select first 2 columns by positions by DataFrame.iloc if need name and surnames:
print (df2)
student name
0 John mills, Tim Harry, Alex win, Kate marry
1 Brando XI, James Caan, Richard S. Castellano
2 Heath Ledger, Aaron Eckhart, Michael Caine
N = 2
df3 = df2["student name"].str.split(', ', expand=True).iloc[:, :N]
#rename columns names
df3.columns = [f"student name_{i+1}" for i in range(len(df3.columns))]
print (df3)
student name_1 student name_2
0 John mills Tim Harry
1 Brando XI James Caan
2 Heath Ledger Aaron Eckhart
Or use list comprehension:
N = 2
L = [x.split(',')[:2] for x in df2["student name"]]
df3 = pd.DataFrame(L, columns=[f"student name_{i+1}" for i in range(N)])
print (df3)
student name_1 student name_2
0 John mills Tim Harry
1 Brando XI James Caan
2 Heath Ledger Aaron Eckhart
If need only names:
N = 2
L = [[y.split()[0] for y in x.split(',')[:2]] for x in df2["student name"]]
df3 = pd.DataFrame(L, columns=[f"student name_{i+1}" for i in range(N)])
print (df3)
student name_1 student name_2
0 John Tim
1 Brando James
2 Heath Aaron
#join to original if necessary
df2 = df2.join(df3)
try this
def find_student(df2):
for i in range(2):
df2[f"student name_{i}"] = pd.Series(map(lambda x: x.split(',')[i], df2["student name"]))
return df2
Use pandas functionality(str and split), you don't need to write a function.
df = [["John mills, Tim Harry, Alex win, Kate marry"],
["Brando XI, James Caan, Richard S. Castellano"],
["Heath Ledger,Aaron Eckhart, Michael Caine"]]
df2 = pd.DataFrame(df)
df2.columns = ['Student_Name']
df2['student name_1'] = df2.Student_Name.str.split(",").str[0]
df2['student name_2'] = df2.Student_Name.str.split(",").str[1]