How To Gather Dataframe Column Into Key Value Pairs As Row In Python

I'm trying to gather a pandas DataFrame column into a key value pairs and list it as a row in python. If we take the following DataFrame as example, I want to go from here: import

Solution 1:

Another solution using melt:

ipdb> pd.melt(df.rename(columns=lambda x: x.split('_')[-1]), var_name="year", value_name="value").dropna()
   yearvalue02016200.012017300.0

Solution 2:

You can create MultiIndex by split and then reshape by stack:

df.columns = df.columns.str.split('_', expand=True)
df = df.stack().reset_index(level=0, drop=True).rename_axis('year').reset_index()
#if necessary convertfloattoint
df.value = df.value.astype(int)
print (df)
   yearvalue0201620012017300

If want use DataFrame constructor use get_level_values:

df.columns = df.columns.str.split('_', expand=True)
df = df.stack()

df_result = pd.DataFrame(OrderedDict({'year': df.index.get_level_values(1),
                                      'value': df['value'].astype(int).values}))

print(df_result)
   year  value
0  2016    200
1  2017    300

Solution 3:

You could use rename, stack and reset_index

In [4912]: (df.rename(columns=lambda x: x.split('_')[-1]).stack()
              .reset_index(level=0, drop=True)
              .rename_axis('year')
              .reset_index(name='value'))
Out[4912]:
   year  value
02016200.012017300.0

Solution 4:

Or using datar:

>>> from datar.allimport f, NA, tribble, pivot_longer, everything, drop_na
>>> >>> df = tribble(
...     f.value_2016, f.value_2017, f.value_2018,
... 200, 300, NA
... )
>>> df
   value_2016  value_2017  value_2018
      <int64>     <int64>   <float64>
0200300         NaN
>>> >>> pivot_longer(df, everything()) >> drop_na()
         name     value
     <object> <float64>
0  value_2016     200.01  value_2017     300.0