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How can I fetch only rows from left dataset but also a certain column from the right dataset, during inner join

I am trying to implement a logic where i have two dataframes. Say A (left) and B (right).
I need to find matching rows of A in B (i understand this can be done via a "inner" join). But my use case says i only need all rows from dataframe A but also a column from the matched record in B, the ID column (i understand this can be one via select). But here the problem arises, as after inner join the returned dataframe has rows from dataframe B also, which i dont need, but if i take leftsemi, then i wont be able to fetch the ID column from B dataframe.
For example:
def fetch_match_condition():
return ["EMAIL","PHONE1"]
match_condition = fetch_match_condition()
from pyspark.sql.functions import sha2, concat_ws
schema_col = ["FNAME","LNAME","EMAIL","PHONE1","ADD1"]
schema_col_master = schema_col.copy()
schema_col_master.append("ID")
data_member = [["ALAN","AARDVARK","lbrennan.kei#malchikzer.gq","7346281938","4176 BARRINGTON CT"],
["DAVID","DINGO","ocoucoumamai#hatberkshire.com","7362537492","2622 MONROE AVE"],
["MICHAL","MARESOVA","annasimkova#chello.cz","435261789","FRANTISKA CERNEHO 61 623 99 UTERY"],
["JAKUB","FILIP","qcermak#email.czanek","8653827956","KOHOUTOVYCH 93 602 42 MLADA BOLESLAV"]]
master_df = spark.createDataFrame(data_member,schema_col)
master_df = master_df.withColumn("ID", sha2(concat_ws("||", *master_df.columns), 256))
test_member = [["ALAN","AARDVARK","lbren.kei#malchik.gq","7346281938","4176 BARRINGTON CT"],
["DAVID","DINGO","ocoucoumamai#hatberkshire.com","99997492","2622 MONROE AVE"],
["MICHAL","MARESOVA","annasimkova#chello.cz","435261789","FRANTISKA CERNEHO 61 623 99 UTERY"],
["JAKUB","FILIP","qcermak#email.czanek","87463829","KOHOUTOVYCH 93 602 42 MLADA BOLESLAV"]]
test_member_1 = [["DAVID","DINGO","ocoucoumamai#hatberkshire.com","7362537492","2622 MONROE AVE"],
["MICHAL","MARESOVA","annasimkova#chello.cz","435261789","FRANTISKA CERNEHO 61 623 99 UTERY"]]
test_df = spark.createDataFrame(test_member,schema_col)
test_df_1 = spark.createDataFrame(test_member_1,schema_col)
matched_df = test_df.join(master_df, match_condition, "inner").select(input_df["*"], master_df["ID"])
master_df = master_df.union(matched_df.select(schema_col_master))
# Here only second last record will match and get added back to the master along with the same ID as it was in master_df
matched_df = test_df_1.join(master_df, match_condition, "inner").select(input_df["*"], master_df["ID"])
# Here the problem arises since i need the match to be only 2, since the test_df_1 has only two records but the match will be 3 since master_df(david record) will also be selected.
PS : (Is there a way i can take leftsemi and then use withColumn and a UDF which fetches me this ID based on the row values of leftsemi dataframe from B dataframe ?)
Can anyone propose a solution for this ?

Python - Matching and extracting data from excel with pandas

I am working on a python script that automates some phone calls for me. I have a tool to test with that I can interact with REST API. I need to select a specific carrier based on which country code is entered. So let's say my user enters 12145221414 in my excel document, I want to choose AT&T as the carrier. How would I accept input from the first column of the table and then output what's in the 2nd column?
Obviously this can get a little tricky, since I would need to match up to 3-4 digits on the front of a phone number. My plan is to write a function that then takes the initial number and then plugs the carrier that needs to be used for that country.
Any idea how I could extract this data from the table? How would I make it so that if you entered Barbados (1246), then Lime is selected instead of AT&T?
Here's my code thus far and tables. I'm not sure how I can read one table and then pull data from that table to use for my matching function.
testlist.xlsx
| Number |
|:------------|
|8155555555|
|12465555555|
|12135555555|
|96655555555|
|525555555555|
carriers.xlsx
| countryCode | Carrier |
|:------------|:--------|
|1246|LIME|
|1|AT&T|
|81|Softbank|
|52|Telmex|
|966|Zain|
import pandas as pd
import os
FILE_PATH = "C:/temp/testlist.xlsx"
xl_1 = pd.ExcelFile(FILE_PATH)
num_df = xl_1.parse('Numbers')
FILE_PATH = "C:/temp/carriers.xlsx"
xl_2 = pd.ExcelFile(FILE_PATH)
car_df = xl_2.parse('Carriers')
for index, row in num_df.iterrows():

Any idea how I could extract this data from the table? How would I
make it so that if you entered Barbados (1246), then Lime is selected
instead of AT&T?
carriers.xlsx
countryCode
Carrier
1246
LIME
1
AT&T
81
Softbank
52
Telmex
966
Zain
script.py
import pandas as pd
FILE_PATH = "./carriers.xlsx"
df = pd.read_excel(FILE_PATH)
rows_list = df.to_dict('records')
code_carrier_map = {}
for row in rows_list:
code_carrier_map[row["countryCode"]] = row["Carrier"]
print(type(code_carrier_map), code_carrier_map)
print(f"{code_carrier_map.get(1)=}")
print(f"{code_carrier_map.get(1246)=}")
print(f"{code_carrier_map.get(52)=}")
print(f"{code_carrier_map.get(81)=}")
print(f"{code_carrier_map.get(966)=}")
Output
$ python3 script.py
<class 'dict'> {1246: 'LIME', 1: 'AT&T', 81: 'Softbank', 52: 'Telmex', 966: 'Zain'}
code_carrier_map.get(1)='AT&T'
code_carrier_map.get(1246)='LIME'
code_carrier_map.get(52)='Telmex'
code_carrier_map.get(81)='Softbank'
code_carrier_map.get(966)='Zain'
Then if you want to parse phone numbers, don't reinvent the wheel, just use this phonenumbers library.
Code
import phonenumbers
num = "+12145221414"
phone_number = phonenumbers.parse(num)
print(f"{num=}")
print(f"{phone_number.country_code=}")
print(f"{code_carrier_map.get(phone_number.country_code)=}")
Output
num='+12145221414'
phone_number.country_code=1
code_carrier_map.get(phone_number.country_code)='AT&T'

Let's assume the following input:
>>> df1
Number
0 8155555555
1 12465555555
2 12135555555
3 96655555555
4 525555555555
>>> df2
countryCode Carrier
0 1246 LIME
1 1 AT&T
2 81 Softbank
3 52 Telmex
4 966 Zain
First we need to rework a bit df2 to sort the countryCode in descending order, make it as string and set it to index.
The trick for later is to sort countryCode in descending order. This will ensure that a longer country codes, such as "1246" is matched before a shorter one like "1".
>>> df2 = df2.sort_values(by='countryCode', ascending=False).astype(str).set_index('countryCode')
>>> df2
Carrier
countryCode
1246 LIME
966 Zain
81 Softbank
52 Telmex
1 AT&T
Finally, we use a regex (here '1246|966|81|52|1' using '|'.join(df2.index)) made from the country codes in descending order to extract the longest code, and we map it to the carrier:
(df1.astype(str)['Number']
.str.extract('^(%s)'%'|'.join(df2.index))[0]
.map(df2['Carrier'])
)
output:
0 Softbank
1 LIME
2 AT&T
3 Zain
4 Telmex
Name: 0, dtype: object
NB. to add it to the initial dataframe:
df1['carrier'] = (df1.astype(str)['Number']
.str.extract('^(%s)'%'|'.join(df2.index))[0]
.map(df2['Carrier'])
).to_clipboard(0)
output:
Number carrier
0 8155555555 Softbank
1 12465555555 LIME
2 12135555555 AT&T
3 96655555555 Zain
4 525555555555 Telmex

If I understand it correctly, you just want to get the first characters from the input column (Number) and then match this with the second dataframe from carriers.xlsx.
Extract first characters of a Number column. Hint: The nbr_of_chars variable should be based on the maximum character length of the column countryCode in the carriers.xlsx
nbr_of_chars = 4
df.loc[df['Number'].notnull(), 'FirstCharsColumn'] = df['Number'].str[:nbr_of_chars]
Then the matching should be fairly easy with dataframe joins.

I can think only of an inefficient solution.
First, sort the data frame of carriers in the reverse alphabetical order of country codes. That way, longer prefixes will be closer to the beginning.
codes = xl_2.sort_values('countryCode', ascending=False)
Next, define a function that matches a number with each country code in the second data frame and finds the index of the first match, if any (remember, that match is the longest).
def cc2carrier(num):
matches = codes['countryCode'].apply(lambda x: num.startswith(x))
if not matches.any(): #Not found
return np.nan
return codes.loc[matches.idxmax()]['Carrier']
Now, apply the function to the numbers dataframe:
xl_1['Number'].apply(cc2carrier)
#1 Softbank
#2 LIME
#3 AT&T
#4 Zain
#5 Telmex
#Name: Number, dtype: object

Forloop with new_values

I have been playing around with a dataset about football, and need to group my ['position'] by values, and assign them to a new variable.
First, here is my dataframe
df = player_stats[['id','player','date','team_name','fixture_name','position','shots', 'shots_on_target', 'xg',
'xa', 'attacking_pen_area_touches', 'penalty_area_entry_passes',
'carries_total_distance', 'total_distance_passed', 'aerial_sucess_perc',
'passes_attempted', 'passes_completed', 'short_pass_accuracy_perc', 'medium_pass_accuracy_perc',
'long_pass_accuracy_perc', 'final_third_entry_passes', 'carries_total_distance', 'ball_recoveries',
'total_distance_passed', 'dribbles_completed', 'dribbles_attempted', 'touches',
'tackles_won', 'tackles_attempted']]
I have split my ['position'] as it had multiple string-values, and added them to a column called ['position_new].
position_new
AM 277
CB 938
CM 534
DF 7
DM 604
FW 766
GK 389
LB 296
LM 149
LW 284
MF 5
RB 300
RM 160
RW 323
WB 275
What I need, is basically to have 3 different variables who have all the same columns, but are separated by the value in the position_new. Look at the below scheme:
So: my variable: Att, need to have all the columns of df, but only with values in position_new that are equal too: FW, LF, RW.
I know how to hardcode it, but cannot get my head around, how to transform it into a for loop.
Here is my loop..
for col in df[29:30]:
if df.loc[df['position_new'] == 'FW', 'LW', 'RW']:
att = df
elif df.loc[df['position_new'] == 'AM', 'CM', 'DM', 'LM', 'RM']:
mid = df
else:
defender = df
Thank you!

I'm not sure what you are trying to do but it looks like you want to get all positions that are of type attackers, midfielders, and defenders based on their two-letter abbreviation into separate variables.
What you are doing is not optimal because it won't work on any generic data frame with this type of info.
But, if you want to do it for just this case, you are simply missing the correct comparison operator in your for loop. Try:
if df.loc[df['position_new'].isin(['FW', 'LW', 'RW'])]:

How to group data in a DataFrame and also show the number of row in that group?

first of all, I have no background in computer language and I am learning Python.
I'm trying to group some data in a data frame.
[dataframe "cafe_df_merged"]
Actually, I want to create a new data frame shows the 'city_number', 'city' (which is a name), and also the number of cafes in the same city. So, it should have 3 columns; 'city_number', 'city' and 'number_of_cafe'
However, I have tried to use the group by but the result did not come out as I expected.
city_directory = cafe_df_merged[['city_number', 'city']]
city_directory = city_directory.groupby('city').count()
city_directory
[the result]
How should I do this? Please help, thanks.

There are likely other ways of doing this as well, but something like this should work:
import pandas as pd
import numpy as np
# Create a reproducible example
places = [[['starbucks', 'new_york', '1234']]*5, [['bean_dream', 'boston', '3456']]*4, \
[['coffee_today', 'jersey', '7643']]*3, [['coffee_today', 'DC', '8902']]*3, \
[['starbucks', 'nowwhere', '2674']]*2]
places = [p for sub in places for p in sub]
# a dataframe containing all information
city_directory = pd.DataFrame(places, columns=['shop','city', 'id'])
# make a new dataframe with just cities and ids
# drop duplicate rows
city_info = city_directory.loc[:, ['city','id']].drop_duplicates()
# get the cafe counts (number of cafes)
cafe_count = city_directory.groupby('city').count().iloc[:,0]
# add the cafe counts to the dataframe
city_info['cafe_count'] = cafe_count[city_info['city']].to_numpy()
# reset the index
city_info = city_info.reset_index(drop=True)
city_info now yields the following:
city id cafe_count
0 new_york 1234 5
1 boston 3456 4
2 jersey 7643 3
3 DC 8902 3
4 nowwhere 2674 2
And part of the example dataframe, city_directory.tail(), looks like this:
shop city id
12 coffee_today DC 8902
13 coffee_today DC 8902
14 coffee_today DC 8902
15 starbucks nowwhere 2674
16 starbucks nowwhere 2674
Opinion: As a side note, it might be easier to get comfortable with regular Python first before diving deep into the world of pandas and numpy. Otherwise, it might be a bit overwhelming.

Using df.query() to extract rows from a DataFrame

I have a DataFrame df which contain three columns: ['mid','2014_amt','2015_amt']
I want to extract rows of a particular merchant. For example, consider my data is:
df['mid'] = ['as','fsd','qww','fd']
df['2014_amt] = [144,232,45,121]
df['2015_amt] = [676,455,455,335]
I want to extract the whole rows corresponding to mid = ['fsd','qww'] How is this best done? I tried with the below code:
df.query('mid== "fsd"')
If I want to run a loop, how can I use the above code to extract rows for specified values of mid?
for val in mid:
print df.query('mid' == "val"')
This is giving an error, as val is not specified.

Option 1
df.query('mid in ["fsd", "qww"]')
mid 2014_amt 2015_amt
1 fsd 232 455
2 qww 45 455
Option 2
df[df['mid'].isin(['fsd', 'qww'])]
mid 2014_amt 2015_amt
1 fsd 232 455
2 qww 45 455