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suppose a dataframe like this one:
df = pd.DataFrame([[1,2,3,4],[5,6,7,8],[9,10,11,12]], columns = ['A', 'B', 'A1', 'B1'])
I would like to have a dataframe which looks like:
what does not work:
new_rows = int(df.shape[1]/2) * df.shape[0]
new_cols = 2
df.values.reshape(new_rows, new_cols, order='F')
of course I could loop over the data and make a new list of list but there must be a better way. Any ideas ?
864
You can use the following basic syntax to convert a pandas DataFrame from a wide format to a long format: df = pd. melt(df, id_vars='col1', value_vars=['col2', 'col3', ...]) In this scenario, col1 is the column we use as an identifier and col2, col3, etc.
In Pandas data reshaping means the transformation of the structure of a table or vector (i.e. DataFrame or Series) to make it suitable for further analysis. Some of Pandas reshaping capabilities do not readily exist in other environments (e.g. SQL or bare bone R) and can be tricky for a beginner.
You can reshape a stacked DataFrame back to its unstacked format with the unstack() function. By default, the innermost level is unstacked. In our example, it was a number. However, you can unstack a different level by passing a level number or name as a parameter to the unstack() method.
You can use lreshape, for column id numpy.repeat:
a = [col for col in df.columns if 'A' in col]
b = [col for col in df.columns if 'B' in col]
df1 = pd.lreshape(df, {'A' : a, 'B' : b})
df1['id'] = np.repeat(np.arange(len(df.columns) // 2), len (df.index)) + 1
print (df1)
A B id
0 1 2 1
1 5 6 1
2 9 10 1
3 3 4 2
4 7 8 2
5 11 12 2
EDIT:
lreshape is currently undocumented, but it is possible it might be removed(with pd.wide_to_long too).
Possible solution is merging all 3 functions to one - maybe melt, but now it is not implementated. Maybe in some new version of pandas. Then my answer will be updated.
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