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Using groupby() for a dataframe in pandas resulted Index Error

I have this dataframe:
x y z parameter
0 26 24 25 Age
1 35 37 36 Age
2 57 52 54.5 Age
3 160 164 162 Hgt
4 182 163 172.5 Hgt
5 175 167 171 Hgt
6 95 71 83 Wgt
7 110 68 89 Wgt
8 89 65 77 Wgt
I'm using pandas to get this final result:
x y parameter
0 160 164 Hgt
1 182 163 Hgt
2 175 167 Hgt
I'm using groupby() to extract and isolate rows based on same parameter Hgt from the original dataframe
First, I added a column to set it as an index:
df = df.insert(0,'index', [count for count in range(df.shape[0])], True)
And the dataframe came out like this:
index x y z parameter
0 0 26 24 25 Age
1 1 35 37 36 Age
2 2 57 52 54.5 Age
3 3 160 164 162 Hgt
4 4 182 163 172.5 Hgt
5 5 175 167 171 Hgt
6 6 95 71 83 Wgt
7 7 110 68 89 Wgt
8 8 89 65 77 Wgt
Then, I used the following code to group based on index and extract the columns I need:
df1 = df.groupby('index')[['x', 'y','parameter']]
And the output was:
x y parameter
0 26 24 Age
1 35 37 Age
2 57 52 Age
3 160 164 Hgt
4 182 163 Hgt
5 175 167 Hgt
6 95 71 Wgt
7 110 68 Wgt
8 89 65 Wgt
After that, I used the following code to isolate only Hgt values:
df2 = df1[df1['parameter'] == 'Hgt']
When I ran df2, I got an error saying:
IndexError: Column(s) ['x', 'y', 'parameter'] already selected
Am I missing something here? What to do to get the final result?

Because you asked what you did wrong, let me point to useless/bad code.
Without any judgement (this is just to help you improve future code), almost everything is incorrect. It feels like a succession of complicated ways to do useless things. Let me give some details:
df = df.insert(0,'index', [count for count in range(df.shape[0])], True)
This seems a very convoluted way to do df.reset_index(). Even [count for count in range(df.shape[0])] could be have been simplified by using range(df.shape[0]) directly.
But this step is not even needed for a groupby as you can group by index level:
df.groupby(level=0)
But... the groupby is useless anyways as you only have single membered groups.
Also, when you do:
df1 = df.groupby('index')[['x', 'y','parameter']]
df1 is not a dataframe but a DataFrameGroupBy object. Very useful to store in a variable when you know what you're doing, this is however causing the error in your case as you thought this was a DataFrame. You need to apply an aggregation or transformation method of the DataFrameGroupBy object to get back a DataFrame, which you didn't (likely because, as seen above, there isn't much interesting to do on single-membered groups).
So when you run:
df1[df1['parameter'] == 'Hgt']
again, all is wrong as df1['parameter'] is equivalent to df.groupby('index')[['x', 'y','parameter']]['parameter'] (the cause of the error as you select twice 'parameter'). Even if you removed this error, the equality comparison would give a single True/False as you still have your DataFrameGroupBy and not a DataFrame, and this would incorrectly try to subselect an inexistent column of the DataFrameGroupBy.
I hope it helped!

Do you really need groupby?
>>> df.loc[df['parameter'] == 'Hgt', ['x', 'y', 'parameter']].reset_index(drop=True)
x y parameter
0 160 164 Hgt
1 182 163 Hgt
2 175 167 Hgt

Replace Not Correctly Removing for Object Variable Type

I'm trying to remove the ":30" portion of values in my First variable. The First variable data type is object.
Here are a few examples of of the First variable, and the counts, ignore the counts:
11a 211
7p 178
4p 127
2:30p 112
11:30a 108
1p 107
12p 105
9a 100
10p 85
2p 24
10:30a 12
6p 5
9:30a 2
9p 2
12:30a 2
8p 2
I wrote the following code which runs without any errors; however, when I run the value counts, it still shows times with a ":30". The NewFirst variable dataype is int64.Not quite sure what I'm doing wrong here.
bad_chars = ":30"
DF["NewFirst"] = DF.First.replace(bad_chars,'')
DF["NewFirst"].value_counts()
The desired output would have the NewFirst values like:
11a 211
7p 178
4p 127
2p 112
11a 108
1p 107
12p 105
9a 100
10p 85
2p 24
10a 12
6p 5
9a 2
9p 2
12a 2
8p 2

You shouldn't be looping over the characters in bad_chars. That will remove all 3 and 0 characters, so 10p will become 1p, and 3a will become a.
You should just replace the whole bad_chars string, with no loop.
You also need to use the .str accessor.
DF["NewFirst"] = DF["First"].str.replace(bad_chars,'')

Write a pandas DataFrame mixing integers and floats in a csv file

I'm working with pandas DataFrames full of float numbers, but with integers in one every three lines (the whole line is made of integers). When I make a print df, all the values displayed are shown as floats (the integers values have a ``.000000```added) for example :
aromatics charged polar unpolar
Ac_obs_counts 712.000000 1486.000000 2688.000000 2792.000000
Ac_obs_freqs 0.092732 0.193540 0.350091 0.363636
Ac_pvalues 0.524752 0.099010 0.356436 0.495050
Am_obs_counts 10.000000 59.000000 62.000000 50.000000
Am_obs_freqs 0.055249 0.325967 0.342541 0.276243
Am_pvalues 0.495050 0.980198 0.356436 0.009901
Ap_obs_counts 18.000000 34.000000 83.000000 78.000000
Ap_obs_freqs 0.084507 0.159624 0.389671 0.366197
Ap_pvalues 0.524752 0.039604 0.980198 0.663366
When I use df.iloc[range(0, len(df.index), 3)], I see integers displayed :
aromatics charged polar unpolar
Ac_obs_counts 712 1486 2688 2792
Am_obs_counts 10 59 62 50
Ap_obs_counts 18 34 83 78
Pa_obs_counts 47 81 125 144
Pf_obs_counts 31 58 99 109
Pg_obs_counts 27 106 102 108
Ph_obs_counts 7 49 42 36
Pp_obs_counts 15 83 45 65
Ps_obs_counts 57 125 170 216
Pu_obs_counts 14 62 102 84
When I use df.to_csv("mydf.csv", sep=",", encoding="utf-8") , the integers are written as floats ; how can I force the writing as integers for these lines ? Would it be better to split the data in two DataFrames ?
Thanks in advance.

Simply call object
df.astype('object')
Out[1517]:
aromatics charged polar unpolar
Ac_obs_counts 712 1486 2688 2792
Ac_obs_freqs 0.092732 0.19354 0.350091 0.363636
Ac_pvalues 0.524752 0.09901 0.356436 0.49505
Am_obs_counts 10 59 62 50
Am_obs_freqs 0.055249 0.325967 0.342541 0.276243
Am_pvalues 0.49505 0.980198 0.356436 0.009901
Ap_obs_counts 18 34 83 78
Ap_obs_freqs 0.084507 0.159624 0.389671 0.366197
Ap_pvalues 0.524752 0.039604 0.980198 0.663366

How to split string with different phones using re?

For example there are such phones:
phones = '+35(123) 456 78 90 (123) 555 55 55 (908)985 88 89 (593)592 56 95'
I need to get:
phones_list = ['+35(123) 456 78 90', '(123) 555 55 55', '(908)985 88 89', (593)592 56 95]
Trying to solve using re, but quite a hard task to me.

This approach uses the + or ( to signal the beginning of a phone number. It does not require multiple-spaces:
>>> phones = '+35(123) 456 78 90 (123) 555 55 55 (908)985 88 89 (593)592 56 95'
>>> re.split(r' +(?=[(+])', phones)
['+35(123) 456 78 90', '(123) 555 55 55', '(908)985 88 89', '(593)592 56 95']
This splits the string based on one-or-more spaces followed by either ( or +.
In the regular expression, + matches one or more spaces. (?=[(+]) is a look-ahead. It requires that the spaces be followed by either ( or + but does not consume the ( or +. Because we are using a look-ahead instead of a plain match, the the leading ( and + remain part of the phone number.

How do i convert a string of ascii values to there original character/number in python

i have a string with numbers that i previously converted with my encoder but now i am trying to decode it ive searched around and no answers seem to work
if you have any i dear how to do this then let me know
string = 91 39 65 97 66 98 67 99 32 49 50 51 39 93
outcome = ABCabc 123

outcome = "".join([your_decoder.decode(x) for x in string.split(" ")])