CODEWITHSUNDEEP
pythondataframe
hyon
hyon

Reputation: 359

make dataframe in python

I would like to create dataframe as below. I tried to create it but there some several for loops and many code lines to do. Is there any efficent way to create dataframe?

what I have:

x=array([a,b,c,d])
y=array([A,B,C,D])
id=array([1,2,3,4])

what I want to have:

df:
id   type   cat
1      a      x
2      b      x
3      c      x
4      d      x
1      A      y
2      B      y
3      C      y
4      D      y

Upvotes: 0

Views: 1467

Answers (2)

Akshay Kandul
Akshay Kandul

Reputation: 602

x = np.array(["a","b","c","d"])
y = np.array(["A","B","C","D"])
id = np.array([1,2,3,4])
df = pd.DataFrame({'id': id, 'x': x, 'y':y})
pd.melt(df, id_vars=['id'], value_vars=['x', 'y'], var_name='cat', value_name='type')

you can use pandas melt function to create your desired output dataframe. Please refer this link pandas.melt for more information on melt

Upvotes: 1

EdChum
EdChum

Reputation: 394159

IIUC you can use concat and pass the 2 dataframes of interest constructed by making a dict with the desired column names and data as the keys, this will vertically stack the dataframes:

In[85]:
x=np.array(['a','b','c','d'])
y=np.array(['A','B','C','D'])
id=np.array([1,2,3,4])
pd.concat([pd.DataFrame({'id':id, 'type':x, 'cat':'x'}, columns=['id','type','cat']), pd.DataFrame({'id':id, 'type':y, 'cat':'y'}, columns=['id','type','cat'])])

Out[85]: 
   id type cat
0   1    a   x
1   2    b   x
2   3    c   x
3   4    d   x
0   1    A   y
1   2    B   y
2   3    C   y
3   4    D   y

Upvotes: 0

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