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Subsetting dataset by using IF statement

I have this massive dataset and I need to subset the data by using criteria. This is for illustration:
| Group | Name | Value |
|--------------------|-------------|-----------------|
| A | Bill| 256 |
| A | Jack| 268 |
| A | Melissa| 489 |
| B | Amanda | 787 |
| B | Eric| 485 |
| C | Matt| 1236 |
| C | Lisa| 1485 |
| D | Ben | 785 |
| D | Andrew| 985 |
| D | Cathy| 1025 |
| D | Suzanne| 1256 |
| D | Jim| 1520 |
I know how to handle this problem manually, such as:
import pandas as pd
df=pd.read_csv('Test.csv')
A=df[df.Group =="A "].to_numpy()
B=df[df.Group =="B "].to_numpy()
C=df[df.Group =="C "].to_numpy()
D=df[df.Group =="D "].to_numpy()
But considering the size of the data, it will take a lot of time if I handle it in this way.
With that in mind, I would like to know if it is possible to build an iteration with an IF statement that can look at the values in column “Group”(table above) . I was thinking, IF statement to see if the first value is the same with one the below if so, group them and create a new array/ dataframe.

Pyspark: Reorder only a subset of rows among themselves

my data frame:
+-----+--------+-------+
| val | id | reRnk |
+-----+--------+-------+
| 2 | a | yes |
| 1 | b | no |
| 3 | c | no |
| 8 | d | yes |
| 7 | e | yes |
| 9 | f | no |
+-----+--------+-------+
In my desired output I will re-rank only the columns where reRnk==yes, ranking will be done based on "val"
I don't want to change the rows where reRnk = no, for example at id=b we have reRnk=no I want to keep that row at row no. 2 only.
my desired output will look like this:
+-----+--------+-------+
| val | id | reRnk |
+-----+--------+-------+
| 8 | d | yes |
| 1 | b | no |
| 3 | c | no |
| 7 | e | yes |
| 2 | a | yes |
| 9 | f | no |
+-----+--------+-------+

From what I'm reading, pyspark DF's do not have an index by default. You might need to add this.
I do not know the exact syntax for pyspark, however since it has many similarities with pandas this might lead you into a certain direction:
df.loc[df.reRnk == 'yes', ['val','id']] = df.loc[df.reRnk == 'yes', ['val','id']].sort_values('val', ascending=False).set_index(df.loc[df.reRnk == 'yes', ['val','id']].index)
Basically what we do here is isolating the rows with reRnk == 'yes', sorting these values but resetting the index to its original index. Then we assign these new values to the original rows in the df.
for .loc, https://spark.apache.org/docs/3.2.0/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.loc.html might be worth a try.
for .sort_values see: https://sparkbyexamples.com/pyspark/pyspark-orderby-and-sort-explained/

Python, Pandas: shrinking a dataframe

inside my application i have a dataframe that looks similiar to this:
Example:
id | address | code_a | code_b | code_c | more columns
1 | parkdrive 1 | 012ah8 | 012ah8a | 1345wqdwqe | ....
2 | parkdrive 1 | 012ah8 | 012ah8a | dwqd4646 | ....
3 | parkdrive 2 | 852fhz | 852fhza | fewf6465 | ....
4 | parkdrive 3 | 456se1 | 456se1a | 856fewf13 | ....
5 | parkdrive 3 | 456se1 | 456se1a | gth8596s | ....
6 | parkdrive 3 | 456se1 | 456se1a | a48qsgg | ....
7 | parkdrive 4 | tg8596 | tg8596a | 134568a | ....
As you may see, every address can contain multiple entrys inside my dataframe, the code_a and code_b are following a certain pattern and only code_c is unqiue.
What I'm trying to obtain is a dataframe where the column code_c is ignored, dropped or whatever and the whole dataframe is reduced to only one entry for each address...something like this:
id | address | code_a | code_b | more columns
1 | parkdrive 1 | 012ah8 | 012ah8a | ...
3 | parkdrive 2 | 852fhz | 852fhza | ...
4 | parkdrive 3 | 456se1 | 456se1a | ...
7 | parkdrive 4 | tg8596 | tg8596a | ...
I tried the groupby-function, but this doesn't seemed to work - or is this even the right function?
Thanks for your help and good day to all of you!

You can drop_duplicates to do this
df.drop_duplicates(subset=[‘address’], inplace=True)
This will keep only a single entry per address

I think what you are looking for is
# in this way you are looking for all the duplicates rows in all columns except for 'code_c'
df.drop_duplicates(subset=df.columns.difference(['code_c']))
# in this way you are looking for all the duplicates rows ONLY based on column 'address'
df.drop_duplicates(subset='address')

I notice in your example data, if you drop columnC then all the entries with address "parkdrive 1" for example, are just duplicates.
you should drop the column c:
df.drop('code_c',axis=1,inplace=True)
Then you can drop the duplicates:
df_clean = df.drop_duplicates()

Merge two datasets based on specific column data

I have two pandas datasets
old:
| alpha | beta | zeta | id | rand | numb|
| ------ | ------------------ | ------------| ------ | --- -| ----|
| 1 | LA | bev | A100 | D | 100 |
| 1 | LA | malib | C150 | Z | 150 |
| 2 | NY | queens | B200 | N | 200 |
| 2 | NY | queens | B200 | N | 200 |
| 3 | Chic | lincpark | E300 | T | 300 |
| 3 | NY | Bronx | F300 | M | 300 |
new:
| alpha | beta | zeta | id | numb |
| ------ | ------------------ | ---------------| ------| -----|
| 1 | LA | Hwood | Q | Q400 |
| 2 | NY | queens | B | B200 |
| 3 | Chic | lincpark | D | D300 |
(Columns and data don't mean anything in particular, just an example).
I want to merge datasets in a way such that
IF old.alpha, old.beta, and old.zeta = their corresponding new columns and If old.id = new.numb, you only keep the entry from the old table. (in this case the row 2 on the old with queens would be kept as opposed to row 2 on new with queens)
Note that rows 3 and 4 on old are the same, but we still keep both. If there were 2 duplicates of these rows in new we consider them as 1-1 corresponding. If maybe there were 3 duplicates on new of rows 3 and 4 on old, then 2 are considered copies (and we don't add them, but we would add the third when we merge them)
IF old.alpha, old.beta, and old.zeta = their corresponding new columns and If old.numb is contained inside new.numb, you only keep the entry from the old table. (in this case the row 5 on the old with lincpark would be kept as opposed to row 3 on new with lincpark, because 300 is contained in new.numb)
Otherwise add the new data as new data, keeping the new table's id and numb, and having null for any extra columns that the old table has (new's row 1 with hollywood)
I have tried various merging methods along with the drop_duplicates method. The problem with the the latter is that I attempted to drop duplicates having the same alpha beta and zeta, but often they were deleted from the same datasource, because the rows were exactly the same.
This is what ultimately needs to be shown when merging. 2 of the rows in new were duplicates, one was something to be added.
| alpha | beta | zeta | id | rand | numb|
| ------ | ------------------ | ------------| ------ | --- -| ----|
| 1 | LA | bev | A100 | D | 100 |
| 1 | LA | malib | C150 | Z | 150 |
| 2 | NY | queens | B200 | N | 200 |
| 2 | NY | queens | B200 | N | 200 |
| 3 | Chic | lincpark | E300 | T | 300 |
| 3 | NY | Bronx | F300 | M | 300 |
| 1 | LA | Hwood | Q | | Q400|

We can merge two Data frames in several ways. Most common way in python is using merge operation in Pandas.
Assuming df1 is your new and df2 is the old
Follow merge by IF conditions.
import pandas
dfinal = df1.merge(df2, on="alpha", how = 'inner')
For merging based on columns of different dataframe, you may specify left and right common column names specially in case of ambiguity of two different names of same column, lets say - 'idold' as 'idnew'.
dfinal = df1.merge(df2, how='inner', left_on='alpha', right_on='id')
If you want to be even more specific, you may read the documentation of pandas merge operation.
Also specify If conditions and perform merge operations by rows, and then drop the remaining columns in a temporary dataframe. And add values to that dataframe according to conditions.
I understand the answer is a little bit complex, but so is your question. Cheers :)

Python - Pandas - Converting column with specific subsets into rows

I have a dataframe that looks like this below with Date, Price and Serial.
+----------+--------+--------+
| Date | Price | Serial |
+----------+--------+--------+
| 2/1/1996 | 0.5909 | 1 |
| 2/1/1996 | 0.5711 | 2 |
| 2/1/1996 | 0.5845 | 3 |
| 3/1/1996 | 0.5874 | 1 |
| 3/1/1996 | 0.5695 | 2 |
| 3/1/1996 | 0.584 | 3 |
+----------+--------+--------+
I will like to make it look like this where the serial becomes the column name and the data sorts itself into the correct date row as well as Serial column.
+----------+--------+--------+--------+
| Date | 1 | 2 | 3 |
+----------+--------+--------+--------+
| 2/1/1996 | 0.5909 | 0.5711 | 0.5845 |
| 3/1/1996 | 0.5874 | 0.5695 | 0.584 |
+----------+--------+--------+--------+
I understand I can do this via a loop but just wondering if there is a more efficient way to do this?
Thanks for your kind help. Also curious if there is a better way to paste such tables rather than attaching images in my questions =x

You can use pandas.pivot_table:
res = df.pivot_table(index='Date', columns='Serial', values='Price', aggfunc=np.sum)\
.reset_index()
res.columns.name = ''
Date 1 2 3
0 2/1/1996 0.5909 0.5711 0.5845
1 3/1/1996 0.5874 0.5695 0.5840