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Fixing broken naming after merging a groupby pivot_table dataframe

I have a problem with naming of columns of dataframe resulting from merging it with its iteration created by group_by.
Generally, the code that creates the mess looks like this:
volume_aggrao = volume.groupby(by = ['room_name', 'material', 'RAO']).sum()['quantity']
volume_aggrao_concat = pd.pivot_table(pd.DataFrame(volume_aggrao), index=['room_name', 'material'], columns = ['RAO'], values = ['quantity'])
volume = volume.merge(volume_aggrao_concat, how = 'left', on = ['room_name', 'material'])
Now to what it does: the goal of pivot_table is to show 'quantity' variable sum over each category of 'RAO' and it looks like that:
And it is fine until you access how it looks on the inside:
"('room_name', '')","('material', '')","('quantity', 'moi')","('quantity', 'nao')","('quantity', 'onrao')","('quantity', 'prom')","('quantity', 'sao')"
1,aluminum,NaN,13.0,NaN,NaN,NaN
1,concrete,151.0,NaN,NaN,NaN,NaN
1,plastic,56.0,NaN,NaN,NaN,NaN
1,steel_mark_1,NaN,30.0,2.0,NaN,1.0
1,steel_mark_2,52.0,NaN,88.0,NaN,NaN
2,aluminum,123.0,NaN,84.0,NaN,NaN
2,concrete,155.0,NaN,NaN,30.0,NaN
2,plastic,170.0,NaN,NaN,NaN,NaN
2,steel_mark_1,107.0,NaN,105.0,47.0,NaN
2,steel_mark_2,81.0,41.0,NaN,NaN,NaN
3,aluminum,NaN,NaN,90.0,NaN,79.0
3,concrete,NaN,82.0,NaN,NaN,NaN
3,plastic,1.0,NaN,25.0,NaN,NaN
3,steel_mark_1,116.0,10.0,NaN,136.0,NaN
3,steel_mark_2,NaN,92.0,34.0,NaN,NaN
4,aluminum,50.0,74.0,NaN,NaN,88.0
4,concrete,96.0,NaN,27.0,NaN,NaN
4,plastic,63.0,135.0,NaN,NaN,NaN
4,steel_mark_1,97.0,NaN,28.0,87.0,NaN
4,steel_mark_2,57.0,22.0,7.0,NaN,NaN
Nevertheless, I was still able to merge it, with resulting columns being named automatically like that:
I cannot seem to be able to call these '(quantity, smth)' columns and hence could not even rename them directly. And there i decided to fully reset column namings with volume.columns = ["id", "room_name", "material", "alpha_UA", "beta_UA", "alpha_F", "beta_F", "gamma_EP", "quantity", "files_id", "all_UA", "RAO", "moi", "nao", "onrao", "prom", "sao"], which is indeed bulky, but it worked. Except it did not when one or more of categorical values of "RAO" is missing. For example, there is no "nao" in "RAO" and hence there is no such column created and hence the code has nothing to rename.
I tried fixing it with volume.rename(lambda x: x.lstrip("(\'quantity\',").strip("\'() \'") if "(" in x else x, axis=1), but it seems to do nothing with them.
I want to know if there is a way to rename these columns.
Data
Here's some example data of 'volume' dataframe you may use to replicate the process with desired output embedded in it to compare
"id","room_name","RAO","moi","nao","onrao","prom","sao"
"1","3","onrao","1","","25","",""
"2","4","nao","57","22","7","",""
"4","2","moi","170","","","",""
"6","4","moi","97","","28","87",""
"7","4","moi","97","","28","87",""
"11","1","nao","","13","","",""
"12","4","onrao","97","","28","87",""
"13","2","moi","107","","105","47",""
"18","2","moi","123","","84","",""
"19","2","moi","155","","","30",""
"22","2","moi","170","","","",""
"23","4","sao","50","74","","","88"
"24","4","nao","50","74","","","88"

So, after a cup of coffee and a cold shower, I was able to investigate a bit further and found out that the strange namings are actually tuples and not strings! Knowing that I decided to iterate over columns to change them to strings and then use the filter. A bit bulky once again, but here is a solution:
for name in volume.columns:
names.append(str(name).lstrip("(\'quantity\',").strip("\'() \'"))

How do I get the top 5 value from groupby and value_counts?

I have a dataset (let's call it df) that looks like this:
What I want to do is to find how the normalized value (via value_counts(normalize = True)*100) of the top 3 nationality per source_type
So, it should look like:
So far I have tried this:
df.groupby(['Source_Type'])['Source_Nationality'].value_counts(normalize=True)*100
But the above code is giving the WHOLE dataset (see below)
So I tried adding .head(3). This one only shows the top 3 of the citizen and not the news part.
(df.groupby(['Source_Type'])['Source_Nationality'].value_counts(normalize = True)*100).head(3)

In your case try head
out = df.groupby(['Source_Type'])['Source_Nationality'].value_counts(normalize=True)*100
out = out.sort_values(ascending=False).groupby(level=0).head(3)

You need to apply head to each of the groups. Try:
>>> df.groupby("Source_Type")["Source_Nationality"].apply(lambda x: x.value_counts(normalize=True).mul(100).head(3))

How to modify multiple values in one column, but skip others in pandas python

Going on two months in python and I am focusing hard on Pandas right now. In my current position I use VBA on data frames, so learning this to slowly replace it and further my career.
As of now I believe my true problem is the lack of understanding a key concept(s). Any help would be greatly appreciated.
That said here is my problem:
Where could I go to learn more on how to do stuff like this for more precise filtering. I'm very close but there is one key aspect I need.
Goal(s)
Main goal I need to skip certain values in my ID column.
The below code takes out the Dashes "-" and only reads up to 9 digits. Yet, I need to skip certain IDs because they are unique.
After that I'll start to work on comparing multiple sheets.
Main data frame IDs is formatted as 000-000-000-000
The other data frames that I will compare it to have it with no
dashes "-" as 000000000 and three less 000's totaling nine digits.
The unique IDs that I need skipped are the same in both data frames, but are formatted completely different ranging from 000-000-000_#12, 000-000-000_35, or 000-000-000_z.
My code that I will use on each ID except the unique ones:
dfSS["ID"] = dfSS["ID"].str.replace("-", "").str[:9]
but I want to use an if statement like (This does not work)
lst = ["000-000-000_#69B", "000-000-000_a", "etc.. random IDs", ]
if ~dfSS["ID"].isin(lst ).any()
dfSS["ID"] = dfSS["ID"].str.replace("-", "").str[:9]
else:
pass
For more clarification my input DataFrame is this:
ID Street # Street Name
0 004-330-002-000 2272 Narnia
1 021-521-410-000_128 2311 Narnia
2 001-243-313-000 2235 Narnia
3 002-730-032-000 2149 Narnia
4 000-000-000_a 1234 Narnia
And I am looking to do this as the output:
ID Street # Street Name
0 004330002 2272 Narnia
1 021-521-410-000_128 2311 Narnia
2 001243313000 2235 Narnia
3 002730032000 2149 Narnia
4 000-000-000_a 1234 Narnia
Notes:
dfSS is my Dataframe variable name aka the excel I am using. "ID" is
my column heading. Will make this an index after the fact
My Data frame on this job is small with # of (rows, columns) as (2500, 125)
I do not get an error message so I am guessing maybe I need a loop of some kind. Starting to test for loops with this as well. no luck there... yet.
Here is where I have been to research this:
Comparison of a Dataframe column values with a list
How to filter Pandas dataframe using 'in' and 'not in' like in SQL
if statement with ~isin() in pandas
recordlinkage module-I didn't think this was going to work
Regular expression operations - Having a hard time fully understanding this at the moment

There are a number of ways to do this. The first way here doesn't involve writing a function.
# Create a placeholder column with all transformed IDs
dfSS["ID_trans"] = dfSS["ID"].str.replace("-", "").str[:9]
dfSS.loc[~dfSS["ID"].isin(lst), "ID"] = dfSS.loc[~dfSS["ID"].isin(lst), "ID_trans"] # conditional indexing
The second way is to write a function that conditionally converts the IDs, and it's not as fast as the first method.
def transform_ID(ID_val):
if ID_val not in lst:
return ID_val.replace("-", "")[:9]
dfSS['ID_trans'] = dfSS['ID'].apply(transform_ID)

This is based on #xyzxyzjayne answers but I have two issues I can not figure out.
First issue
is I get this warning: (see Edit)
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
Documentation for this warning
You'll see in the code below I tried to put in .loc but I can't seem to find out how to eliminate this warning by using .loc correctly. Still learning it. NO, I will not just ignore it even though it works. This is a learning opportunity I say.
Second issue
is that I do not under stand this part of the code. I know the left side is supposed to be rows, and the right side is columns. That said why does this work? ID is a column not a row when this code is rune. I make the ID :
df.loc[~df["ID "].isin(uniqueID ), "ID "] = df.loc[~df["ID "].isin(uniqueID ), "Place Holder"]
Area I don't understand yet, is the left side of the comma(,) on this part:
df.loc[~df["ID "].isin(uniqueID), "ID "]
That said here is the final result, basically as I Said its XZY's help that got me here but I'm adding more .locs and playing with the documentation till I can eliminate the warning.
uniqueID = [ and whole list of IDs i had to manually enter 1000+ entries that
will go in the below code. These ids get skipped. example: "032-234-987_#4256"]
# gets the columns i need to make the DateFrame smaller
df = df[['ID ', 'Street #', 'Street Name', 'Debris Finish', 'Number of Vehicles',
'Number of Vehicles Removed', 'County']]
#Place holder will make our new column with this filter
df.loc[:, "Place Holder"] = df.loc[:,"ID "].str.replace("-", "").str[:9]
#the next code is the filter that goes through the list and skips them. Work in progress to fully understand.
df.loc[~df["ID "].isin(uniqueID ), "ID "] = df.loc[~df["ID "].isin(uniqueID ), "Place Holder"]
#Makes the ID our index
df = df.set_index("ID ")
#just here to add the date to our file name. Must import time for this to work
todaysDate = time.strftime("%m-%d-%y")
#make it an excel file
df.to_excel("ID TEXT " + todaysDate + ".xlsx")
Will edit this once i get rid of the warning and figure out the left side so I can explain to for everyone who needs/sees this post.
Edit: SettingWithCopyWarning:
Fixed this chained index problem by making a copy of the orginal data base before filter and making everthing .loc as XYZ has helped me with. Before we start to filter use DataFrame.copy() where DataFrame is the name of your own dataframe.

Counting Frequency of an Aggregate result using pandas

Broadly I have the Smart Meters dataset from Kaggle and I'm trying to get a count of the first and last measure by house, then trying to aggregate that to see how many houses began (or ended) reporting on a given day. I'm open to methods totally different than the line I pursue below.
In SQL, when exploring data I often used something like following:
SELECT Max_DT, COUNT(House_ID) AS HouseCount
FROM
(
SELECT House_ID, MAX(Date_Time) AS Max_DT
FROM ElectricGrid GROUP BY HouseID
) MeasureMax
GROUP BY Max_DT
I'm trying to replicate this logic in Pandas and failing. I can get the initial aggregation like:
house_max = house_info.groupby('House_Id').agg({'Date_Time' :['max']})
However I'm failing to get the outer query. Specifically I don't know what the aggregated column is called. If I do a describe() it shows as Date_Time in the example above. I tried renaming the columns:
house_max.columns = ['House_Id','Max_Date_Time']
I found a StackOverflow discussion about renaming the results of aggregation and attempted to apply it:
house_max.columns = ["_".join(x) for x in house_max.columns.ravel()]
I still find that a describe() returns Date_Time as the column name.
start_end_collate = house_max.groupby('Date_Time_max')['House_Id'].size()
In the rename example my second query fails to find Date_Time or Max_Date_Time. In the later case, the Ravel code it appears to not find House_Id when I run it.

That's seems weird, I would think your code would not be able to find the House_Id field. After you perform your groupby on House_Id it becomes an index which you cannot reference as a column.
This should work:
house_max = house_info.groupby('House_Id').agg({'Date_Time' :['max']})
house_max.columns = ["_".join(x) for x in house_max.columns.ravel()]
start_end_collate = house_max.groupby('Date_Time_max').size()
Alternatively you can just drop the multilevel column:
house_max.columns = house_max.columns.droplevel(0)
start_end_collate = house_max.groupby('max').size()

Iterating through dataframe to produce chart title (Python)

Good morning,
I am trying to iterate through a CSV to produce a title for each stock chart that I am making.
The CSV is formatted as: Ticker, Description spanning about 200 rows.
The code is shown below:
df_symbol_description = pd.read_csv('C:/TS/Combined/Tickers Desc.csv')
print(df_symbol_description['Description'])
for r in df_symbol_description['Description']:
plt.suptitle(df_symbol_description['Description'][r],size = '20')
It is erroneous as it comes back with this error: "KeyError: 'iShrs MSCI ACWI ETF'"
This error is just showing me the first ticker description in the CSV. If anyone knows how to fix this is is much appreciated!
Thank you

I don't know how to fix the error, since it's unclear what you are trying to achieve, but we can have a look at the problem itself.
Consider this example, which is essentially your code in small.
import pandas as pd
df=pd.DataFrame({"x" : ["A","B","C"]})
for r in df['x']:
print(r, df['x'][r])
The dataframe consists of one column, called x which contains the values "A","B","C". In the for loop you select those values, such that for the first iteration r is "A". You are then using "A" as an index to the column, which is not possible, since the column would need to be indexed by 0,1 or 2, but not the string that it contains.
So in order to print the column values, you can simply use
for r in df['x']:
print(r)