I have this df dataset:

df = pd.DataFrame({'train': {'auc': [0.432,       0.543,       0.523],
  'logloss': [0.123,       0.234,       0.345]},
 'test': {'auc': [0.456,       0.567,       0.678],
  'logloss': [0.321,       0.432,       0.543]}})

Where I'm trying to transform it into this:

And also considering that:

• epochs always have the same order for every cell, but instead of only 3 epochs, it could reach 1.000 or 10.000.
• The column names and axis could change. For example another day the data could have f1 instead of logloss, or val instead of train. But no matter the names, in df each row will always be a metric name, and each column will always be a dataset name.
• The number of columns and rows in df could change too. There are some models with 5 datasets, and 7 metrics for example (which would give a df with 5 columns and 7 rows)
• The columname of the output table should be datasetname_metricname

So I'm trying to build some generic code transformation where at the same time avoiding brute force transformations. Just if it's helpful, the df source is:

df = pd.DataFrame(model_xgb.evals_result())
df.columns = ['train', 'test'] # This is the line that can change (and the metrics inside `model_xgb`)

Where model_xgb = xgboost.XGBClassifier(..), but after using model_xgb.fit(..)

CodePudding user response：

Here's a generic way to get the result you've specified, irrespective of the number of epochs or the number or labels of rows and columns:

df2 = df.stack().apply(pd.Series)
df2.index = ['_'.join(reversed(x)) for x in df2.index]
df2 = df2.T.assign(epochs=range(1, len(df2.columns)   1)).set_index('epochs').reset_index()

Output:

   epochs  train_auc  test_auc  train_logloss  test_logloss
0       1      0.432     0.456          0.123         0.321
1       2      0.543     0.567          0.234         0.432
2       3      0.523     0.678          0.345         0.543

Explanation:

• Use stack() to convert the input dataframe to a series (of lists) with a multiindex that matches the desired column sequence in the question
• Use apply(pd.Series) to convert the series of lists to a dataframe with each list converted to a row and with column count equal to the uniform length of the list values in the input series (in other words, equal to the number of epochs)
• Create the desired column labels from the latest multiindex rows transformed using join() with _ as a separator, then use T to transpose the dataframe so these index labels (which are the desired column labels) become column labels
• Use assign() to add a column named epochs enumerating the epochs beginning with 1
• Use set_index() followed by reset_index() to make epochs the leftmost column.

CodePudding user response：

Try this:

df = pd.DataFrame({'train': {'auc': [0.432,       0.543,       0.523],
  'logloss': [0.123,       0.234,       0.345]},
 'test': {'auc': [0.456,       0.567,       0.678],
  'logloss': [0.321,       0.432,       0.543]}})

de=df.explode(['train', 'test'])
df_out = de.set_index(de.groupby(level=0).cumcount() 1, append=True).unstack(0)
df_out.columns = df_out.columns.map('_'.join)
df_out = df_out.reset_index().rename(columns={'index':'epochs'})
print(df_out)

Output:

   epochs train_auc train_logloss test_auc test_logloss
0       1     0.432         0.123    0.456        0.321
1       2     0.543         0.234    0.567        0.432
2       3     0.523         0.345    0.678        0.543