CODEWITHSUNDEEP
pythonstringpandassplit
Zophai
Zophai

Reputation: 104

How to split concatenated column name into separate columns?

In order to perform an analysis, I have been provided with a column name which contains specific information about the product, market and distribution.

The structure of the dataset is as follows:

Date     Product1|CBA|MKD  Product1|CPA|MKD     Product1|CBA|IHR    Product2|CBA|IHR
2018-11  12                 23                   0                   2

There are a lot of unique column combinations. What I would like to do is to get the following structure:

Date      Product    Partner   Market      Quantity
2020-1    Product1   CBA       MKD         11
2020-1    Product1   CPA       MKD         22
2020-1    Product1   CBA       IHR         0
2020-1    Product2   CBA       IHR         1

So, I want to create 3 different columns and populate them with pasted values from the column name. The quantity column would obviously contain the value of the old concatenated column (that bit I know how to do), the issue is getting the first 3 columns.

I have tried to do this in pandas by matching strings but I am really stuck. I'd appreciate some help, thank you!

Upvotes: 1

Views: 134

Answers (3)

Ayoub ZAROU
Ayoub ZAROU

Reputation: 2417

It looks like you could use pandas.melt

df_ = df.melt(id_vars = 'Date', value_name = 'Quantity')
df_[['Product', 'Partner','Market']] = df_.variable.str.split('|', 
                                                             expand = True)\
                                                        .dropna(axis = 1) 
df_.pop('variable')

df_
Out[67]: 
      Date  Quantity   Product Partner Market
0  2018-11        12  Product1     CBA    MKD
1  2018-11        23  Product1     CPA    MKD
2  2018-11         0  Product1     CBA    IHR
3  2018-11         2  Product2     CBA    IHR

Upvotes: 3

Akaisteph7
Akaisteph7

Reputation: 6476

Here's another way to do it:

st = df.set_index("Date").stack().reset_index(-1)
res = st["level_1"].str.split("|")
st[["Product","Partner","Market"]] = pd.DataFrame(res.tolist(), index=st.index)
df2 = st.drop("level_1", axis=1).rename({0:"Quantity"}, axis=1)

print(df2)

         Quantity   Product Partner Market
Date
2018-11        12  Product1     CBA    MKD
2018-11        23  Product1     CPA    MKD
2018-11         0  Product1     CBA    IHR
2018-11         2  Product2     CBA    IHR

Upvotes: 2

moys
moys

Reputation: 8033

a = df.melt(id_vars=["Date"],var_name="Product", 
    value_name="Val").dropna(how='any').sort_values('Date')
a['Partner'] = a['Product'].str.split("|").str[1]
a['Market'] = a['Product'].str.split("|").str[-1]
a['Product']= a['Product'].str.split("|").str[0]

Upvotes: 1

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