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Create the same ids for the same names in different dataframes in pandas

I have a dataset with unique names. Another dataset contains several rows with the same names as in the first dataset.
I want to create a column with unique ids in the first dataset and another column in the second dataset with the same ids corresponding to all the same names in the first dataset.
For example:
Dataframe 1:
player_id Name
1 John Dosh
2 Michael Deesh
3 Julia Roberts
Dataframe 2:
player_id Name
1 John Dosh
1 John Dosh
2 Michael Deesh
2 Michael Deesh
2 Michael Deesh
3 Julia Roberts
3 Julia Roberts
I want to do to use both data frames to run deep feature synthesis using featuretools.
To be able to do something like this:
entity_set = ft.EntitySet("basketball_players")
entity_set.add_dataframe(dataframe_name="players_set",
dataframe=players_set,
index='name'
)
entity_set.add_dataframe(dataframe_name="season_stats",
dataframe=season_stats,
index='season_stats_id'
)
entity_set.add_relationship("players_set", "player_id", "season_stats", "player_id")

This should do what your question asks:
import pandas as pd
df1 = pd.DataFrame([
'John Dosh',
'Michael Deesh',
'Julia Roberts'], columns=['Name'])
df2 = pd.DataFrame([
['John Dosh'],
['John Dosh'],
['Michael Deesh'],
['Michael Deesh'],
['Michael Deesh'],
['Julia Roberts'],
['Julia Roberts']], columns=['Name'])
print('inputs:', '\n')
print(df1)
print(df2)
df1 = df1.reset_index().rename(columns={'index':'id'}).assign(id=df1.index + 1)
df2 = df2.join(df1.set_index('Name'), on='Name')[['id'] + list(df2.columns)]
print('\noutputs:', '\n')
print(df1)
print(df2)
Input/output:
inputs:
Name
0 John Dosh
1 Michael Deesh
2 Julia Roberts
Name
0 John Dosh
1 John Dosh
2 Michael Deesh
3 Michael Deesh
4 Michael Deesh
5 Julia Roberts
6 Julia Roberts
outputs:
id Name
0 1 John Dosh
1 2 Michael Deesh
2 3 Julia Roberts
id Name
0 1 John Dosh
1 1 John Dosh
2 2 Michael Deesh
3 2 Michael Deesh
4 2 Michael Deesh
5 3 Julia Roberts
6 3 Julia Roberts
UPDATE:
An alternative solution which should give the same result is:
df1 = df1.assign(id=list(range(1, len(df1) + 1)))[['id'] + list(df1.columns)]
df2 = df2.merge(df1)[['id'] + list(df2.columns)]

Pandas: a Pythonic way to create a hyperlink from a value stored in another column of the dataframe

I have the following toy dataset df:
import pandas as pd
data = {
'id' : [1, 2, 3],
'name' : ['John Smith', 'Sally Jones', 'William Lee']
}
df = pd.DataFrame(data)
df
id name
0 1 John Smith
1 2 Sally Jones
2 3 William Lee
My ultimate goal is to add a column that represents a Google search of the value in the name column.
I do this using:
def create_hyperlink(search_string):
return f'https://www.google.com/search?q={search_string}'
df['google_search'] = df['name'].apply(create_hyperlink)
df
id name google_search
0 1 John Smith https://www.google.com/search?q=John Smith
1 2 Sally Jones https://www.google.com/search?q=Sally Jones
2 3 William Lee https://www.google.com/search?q=William Lee
Unfortunately, newly created google_search column is returning a malformed URL. The URL should have a "+" between the first name and last name.
The google_search column should return the following:
https://www.google.com/search?q=John+Smith
It's possible to do this using split() and join().
foo = df['name'].str.split()
foo
0 [John, Smith]
1 [Sally, Jones]
2 [William, Lee]
Name: name, dtype: object
Now, joining them:
df['bar'] = ['+'.join(map(str, l)) for l in df['foo']]
df
id name google_search foo bar
0 1 John Smith https://www.google.com/search?q=John Smith [John, Smith] John+Smith
1 2 Sally Jones https://www.google.com/search?q=Sally Jones [Sally, Jones] Sally+Jones
2 3 William Lee https://www.google.com/search?q=William Lee [William, Lee] William+Lee
Lastly, creating the updated google_search column:
df['google_search'] = df['bar'].apply(create_hyperlink)
df
Is there a more elegant, streamlined, Pythonic way to do this?
Thanks!

Rather than reinvent the wheel and modify your string manually, use a library that's guaranteed to give you the right result :
from urllib.parse import quote_plus
def create_hyperlink(search_string):
return f"https://www.google.com/search?q={quote_plus(search_string)}"

Use Series.str.replace:
df['google_search'] = 'https://www.google.com/search?q=' + \
df.name.str.replace(' ','+')
print(df)
id name google_search
0 1 John Smith https://www.google.com/search?q=John+Smith
1 2 Sally Jones https://www.google.com/search?q=Sally+Jones
2 3 William Lee https://www.google.com/search?q=William+Lee

how to add multiple values ​and make the row repeat for the number of values

I have a list of objects by each name and a dataframe like this.
Jimmy = ['chair','table','pencil']
Charles = ['smartphone','cake']
John = ['clock','paper']
id
name
1
Jimmy
2
Charles
3
John
I would like to use a loop that allows me to obtain the following result.
id
name
picks
1
Jimmy
chair
1
Jimmy
table
1
Jimmy
pencil
2
Charles
smartphone
2
Charles
cake
3
John
clock
3
John
paper

You can assign and explode:
values = {'Jimmy': Jimmy, 'Charles': Charles, 'John': John}
out = df.assign(picks=df['name'].map(values)).explode('picks')
Or set up a DataFrame, stack and merge:
values = {'Jimmy': Jimmy, 'Charles': Charles, 'John': John}
out = df.merge(
pd.DataFrame.from_dict(values, orient='index')
.stack().droplevel(1).rename('picks'),
left_on='name', right_index=True
)
output:
id name picks
0 1 Jimmy chair
0 1 Jimmy table
0 1 Jimmy pencil
1 2 Charles smartphone
1 2 Charles cake
2 3 John clock
2 3 John paper

We can make a dataframe relating names to picks, then join them together with merge:
import pandas as pd
#dataframe from question
df = pd.DataFrame()
df["id"] = [1, 2, 3]
df["name"] = ["Jimmy", "Charles", "John"]
#dataframe relating names to picks.
picks_df = pd.DataFrame()
picks_df["name"] = ["Jimmy", "Jimmy", "Jimmy", "Charles", "Charles", "John", "John"]
picks_df["picks"] = ["chair", "table", "pencil", "smartphone", "cake", "clock", "paper"]
#Merge and print
print(pd.merge(df, picks_df, on="name"))

I am trying to groupby values in a specific column that holds multiple values?

I have this huge netflix dataset which I am trying to see which actors appeared in the most movies/tv shows specifically in America. First, I created a list of unique actors from the dataset. Then created a nested for loop to loop through each name in list3(containing unique actors which checked each row in df3(filtered dataset with 2000+rows) if the column cast contained the current actors name from list3. I believe using iterrows takes too long
myDict1 = {}
for name in list3:
if name not in myDict1:
myDict1[name] = 0
for index, row in df3.iterrows():
if name in row["cast"]:
myDict1[name] += 1
myDict1
Title
cast
Movie1
Robert De Niro, Al Pacino, Tarantino
Movie2
Tom Hanks, Robert De Niro, Tom Cruise
Movie3
Tom Cruise, Zendaya, Seth Rogen
I want my output to be like this:
Name
Count
Robert De Niro
2
Tom Cruise
2

Use
out = df['cast'].str.split(', ').explode().value_counts()
out = pd.DataFrame({'Name': out.index, 'Count': out.values})
>>> out
Name Count
0 Tom Cruise 2
1 Robert De Niro 2
2 Zendaya 1
3 Seth Rogen 1
4 Tarantino 1
5 Al Pacino 1
6 Tom Hanks 1

l=['Robert De Niro','Tom Cruise']#list
df=df.assign(cast=df['cast'].str.split(',')).apply(pd.Series.explode)#convert cast into list and explode
df[df['cast'].str.contains("|".join(l))].groupby('cast').size().reset_index().rename(columns={'cast':'Name',0:'Count'})#groupby cast, find size and rename columns
Name Count
0 Robert De Niro 2
1 Tom Cruise 2

You could use collections.Counter to get the counts of the actors, after splitting the strings:
from collections import Counter
pd.DataFrame(Counter(df.cast.str.split(", ").sum()).items(),
columns = ['Name', 'Count'])
Name Count
0 Robert De Niro 2
1 Al Pacino 1
2 Tarantino 1
3 Tom Hanks 1
4 Tom Cruise 2
5 Zendaya 1
6 Seth Rogen 1
If you are keen about speed, and you have lots of data, you could dump the entire processing within plain python and rebuild the dataframe:
from itertools import chain
pd.DataFrame(Counter(chain.from_iterable(ent.split(", ")
for ent in df.cast)).items(),
columns = ['Name', 'Count'])

Analyzing Token Data from a Pandas Dataframe

I'm a relative python noob and also new to natural language processing (NLP).
I have dataframe containing names and sales. I want to: 1) break out all the tokens and 2) aggregate sales by each token.
Here's an example of the dataframe:
name sales
Mike Smith 5
Mike Jones 3
Mary Jane 4
Here's the desired output:
token sales
mike 8
mary 4
Smith 5
Jones 3
Jane 4
Thoughts on what to do? I'm using Python.

Assumption: you have a function tokenize that takes in a string as input and returns a list of tokens
I'll use this function as a tokenizer for now:
def tokenize(word):
return word.casefold().split()
Solution
df.assign(tokens=df['name'].apply(tokenize)).explode('tokens').groupby('tokens')['sales'].sum().reset_index()
In [45]: df
Out[45]:
name sales
0 Mike Smith 5
1 Mike Jones 3
2 Mary Jane 4
3 Mary Anne Jane 1
In [46]: df.assign(tokens=df['name'].apply(tokenize)).explode('tokens').groupby('tokens')['sales'].sum().reset_index()
Out[46]:
tokens sales
0 anne 1
1 jane 5
2 jones 3
3 mary 5
4 mike 8
5 smith 5
Explanation
The assign step creates a column called tokens that applies the tokenize functio
Note: For this particular tokenize function - you can use df['name'].str.lower().str.split() - however this won't generalize to custom tokenizers hence the .apply(tokenize)
this generates a df that looks like
name sales tokens
0 Mike Smith 5 [mike, smith]
1 Mike Jones 3 [mike, jones]
2 Mary Jane 4 [mary, jane]
3 Mary Anne Jane 1 [mary, anne, jane]
use df.explode on this to get
name sales tokens
0 Mike Smith 5 mike
0 Mike Smith 5 smith
1 Mike Jones 3 mike
1 Mike Jones 3 jones
2 Mary Jane 4 mary
2 Mary Jane 4 jane
3 Mary Anne Jane 1 mary
3 Mary Anne Jane 1 anne
3 Mary Anne Jane 1 jane
last step is just a groupy-agg step.

You can use the str.split() method and keep item 0 for the first name, using that as the groupby key and take the sum, then do the same for item -1 (last name) and concatenate the two.
import pandas as pd
df = pd.DataFrame({'name': {0: 'Mike Smith', 1: 'Mike Jones', 2: 'Mary Jane'},
'sales': {0: 5, 1: 3, 2: 4}})
df = pd.concat([df.groupby(df.name.str.split().str[0]).sum(),
df.groupby(df.name.str.split().str[-1]).sum()]).reset_index()
df.rename(columns={'name':'token'}, inplace=True)

df[["fname", "lname"]] = df["name"].str.split(expand=True) # getting tokens,considering separated by space
tokens_df = pd.concat([df[['fname', 'sales']].rename(columns = {'fname': 'tokens'}),
df[['lname', 'sales']].rename(columns = {'lname': 'tokens'})])
pd.DataFrame(tokens_df.groupby('tokens')['sales'].apply(sum), columns=['sales'])