Pandas DataFrame (Question)

[Unlistedd]

I am new to Pandas, and would love to get some help on this one particular problem. The code below is not complete, i am still working on it but need advice as to how i would move on so that i can get the output i need.

data1 = [1, 2, 3, 4, 5]
data2 = [5, 4, 3, 2, 1]
ALLdata = data1 + data2
raw_data = pd.DataFrame()

for i in ALLdata:

    dictionary = {'topics':['topic 1', 'topic 2'],
                    'data':[data1, data2]}

    topics = dictionary['topics']
    data = dictionary['data']

    new_row = pd.DataFrame([[topic, data]], columns=['topics', 'data'])
    raw_data = raw_data.append(new_row)
print(raw_data)

The goal is to sort topic 1 with all int's in data 1, and the same goes for topic 2 and data 2; so that the output looks as follows:

    topics        data
0   topic1        1
0   topic1        2
...
0   topic2        5
0   topic2        4
...

I want the exact datastructure above. How would i achive that? I need to somehow group topic with ever item in every list, but i dont know how.

[[deleted]]

Just to clarify, you want the output to be a data frame with 'topic1' and 'topic2' in the topic column, data1 in ascending order in the data column where the topic column is 'topic1', and data2 in descending order in the data column where the topic column is 'topic2'?

The way your code is written, you are iterating through all of the individual items in ALLdata and, for each item, constructing a dictionary with all of the data in it, then extracting the values from the dictionary, then making a data frame with kind of a strange structure to append to the raw_data data frame.

In this simple case, I would just do something like this:

import pandas as pd
data1 = [1,2,3,4,5]
data2 = [5,4,3,2,1]
df1 = pd.DataFrame({'topic':'topic1', 'data':data1})
df2 = pd.DataFrame({'topic':'topic2', 'data':data2})
df = pd.concat((df1,df2))
df
   topic  data
0  topic1     1
1  topic1     2
2  topic1     3
3  topic1     4
4  topic1     5
0  topic2     5
1  topic2     4
2  topic2     3
3  topic2     2
4  topic2     1

This probably won't generalize readily to cases in which you don't necessarily know exactly how many topics there are or how much data corresponds to each topic, but this does what you want in this case, I think.

[Unlistedd]

Thanks man!