Simplifying conditional arrays from a text file pandas python

Question

I'm trying to access data from a textfile and apply things such as normal tests, confidence intervals, ANOVA tests and so forth.

Is there an easier way of creating conditional arrays using pandas from my data without manualing typing out 36 lines of code as I have done below?

Later I will need to access different flavours within these packs, so I will need to do the formulation about 7 times otherwise.

revels_data = pd.read_csv("revels2.txt")
rd = revels_data

# packet sums
total_1 = (rd.loc[rd["Packet number"] == 1, "Contents"].sum())
total_2 = (rd.loc[rd["Packet number"] == 2, "Contents"].sum())
total_3 = (rd.loc[rd["Packet number"] == 3, "Contents"].sum())
total_4 = (rd.loc[rd["Packet number"] == 4, "Contents"].sum())
total_5 = (rd.loc[rd["Packet number"] == 5, "Contents"].sum())
total_6 = (rd.loc[rd["Packet number"] == 6, "Contents"].sum())
total_7 = (rd.loc[rd["Packet number"] == 7, "Contents"].sum())
total_8 = (rd.loc[rd["Packet number"] == 8, "Contents"].sum())
total_9 = (rd.loc[rd["Packet number"] == 9, "Contents"].sum())
total_10 = (rd.loc[rd["Packet number"] == 10, "Contents"].sum())
total_11 = (rd.loc[rd["Packet number"] == 11, "Contents"].sum())
total_12 = (rd.loc[rd["Packet number"] == 12, "Contents"].sum())
total_13 = (rd.loc[rd["Packet number"] == 13, "Contents"].sum())
total_14 = (rd.loc[rd["Packet number"] == 14, "Contents"].sum())
total_15 = (rd.loc[rd["Packet number"] == 15, "Contents"].sum())
total_16 = (rd.loc[rd["Packet number"] == 16, "Contents"].sum())
total_17 = (rd.loc[rd["Packet number"] == 17, "Contents"].sum())
total_18 = (rd.loc[rd["Packet number"] == 18, "Contents"].sum())
total_19 = (rd.loc[rd["Packet number"] == 19, "Contents"].sum())
total_20 = (rd.loc[rd["Packet number"] == 20, "Contents"].sum())
total_21 = (rd.loc[rd["Packet number"] == 21, "Contents"].sum())
total_22 = (rd.loc[rd["Packet number"] == 22, "Contents"].sum())
total_23 = (rd.loc[rd["Packet number"] == 23, "Contents"].sum())
total_24 = (rd.loc[rd["Packet number"] == 24, "Contents"].sum())
total_25 = (rd.loc[rd["Packet number"] == 25, "Contents"].sum())
total_26 = (rd.loc[rd["Packet number"] == 26, "Contents"].sum())
total_27 = (rd.loc[rd["Packet number"] == 27, "Contents"].sum())
total_28 = (rd.loc[rd["Packet number"] == 28, "Contents"].sum())
total_29 = (rd.loc[rd["Packet number"] == 29, "Contents"].sum())
total_30 = (rd.loc[rd["Packet number"] == 30, "Contents"].sum())
total_31 = (rd.loc[rd["Packet number"] == 31, "Contents"].sum())
total_32 = (rd.loc[rd["Packet number"] == 32, "Contents"].sum())
total_33 = (rd.loc[rd["Packet number"] == 33, "Contents"].sum())
total_34 = (rd.loc[rd["Packet number"] == 34, "Contents"].sum())
total_35 = (rd.loc[rd["Packet number"] == 35, "Contents"].sum())
total_36 = (rd.loc[rd["Packet number"] == 36, "Contents"].sum())

# create total array
a = np.array([total_1, total_2, total_3, total_4, total_5, total_6, total_7,
total_8, total_9, total_10, total_11, total_12, total_13, total_14, total_15,
total_16, total_17, total_18, total_19, total_20, total_21, total_22, total_23,
total_24, total_25, total_26, total_27, total_28, total_29, total_30, total_31,
total_32, total_33, total_34, total_35, total_36])

# mean confidence interval
print(st.t.interval(0.95, len(a)-1, loc=np.mean(a), scale=st.sem(a)))

Thanks!

EDIT:

dataset looks like:

Packet number,Flavour,Contents
1,orange,4
2,orange,3
3,orange,2
4,orange,4
5,orange,3
...
36,orange,3
1,toffee,4
2,toffee,3
...
1,chocolate,5
...

etc.

desired data:

for each of the flavour types i want an array/list of the contents to analyse, i.e.

for orange:

4
3
2
4
...

so i can then apply various tests on these newly created arrays

Answer 1

IIUC you can do the following.

If you have only 36 distinct values (from 1 to 36) in the Packet number column:

a = rd.groupby('Packet number')['Contents'].sum()

If you have more and want to filter them first:

a = rd[rd['Packet number'].between(1, 36)].groupby('Packet number')['Contents'].sum()

---

UPDATE:

Source DF

In [233]: df
Out[233]:
   Packet number    Flavour  Contents
0              1     orange         4
1              2     orange         3
2              3     orange         2
3              4     orange         4
4              5     orange         3
5             36     orange         3
6              1     toffee         4
7              2     toffee         3
8              1  chocolate         5

simple boolean indexing

In [234]: df.loc[df.Flavour == 'orange', 'Contents']
Out[234]:
0    4
1    3
2    2
3    4
4    3
5    3
Name: Contents, dtype: int64

... plus sum

In [235]: df.loc[df.Flavour == 'orange', 'Contents'].sum()
Out[235]: 19

filter, groupby, aggregate

In [237]: df.loc[df.Flavour.isin(['orange','toffee'])].groupby('Flavour')['Contents'].sum()
Out[237]:
Flavour
orange    19
toffee     7
Name: Contents, dtype: int64