How to Convert Index of a Pandas Dataframe into a Column

How to convert index of a pandas dataframe into a column

either:

df['index1'] = df.index

or, .reset_index:

df = df.reset_index(level=0)

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so, if you have a multi-index frame with 3 levels of index, like:

>>> df
                       val
tick       tag obs        
2016-02-26 C   2    0.0139
2016-02-27 A   2    0.5577
2016-02-28 C   6    0.0303

and you want to convert the 1st (tick) and 3rd (obs) levels in the index into columns, you would do:

>>> df.reset_index(level=['tick', 'obs'])
          tick  obs     val
tag                        
C   2016-02-26    2  0.0139
A   2016-02-27    2  0.5577
C   2016-02-28    6  0.0303

convert index to column pandas dataframe

Need assign output back or inplace=True parameter:

df = df.reset_index()

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df.reset_index(inplace=True)

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print (df)
              datetime           id         LocalTime  ZPosition  XPosition
0  2017-01-02 00:14:39  10453190861  1483312478909238          0         -9
1  2017-01-02 00:14:40  10453191020  1483312479673076          0         -8

How can I convert an index column and add it back to the dataframe?

You can use melt:

out = (df.melt(['Index1', 'Index2'], var_name='Category', value_name='Value')
         .assign(Category=lambda x: x['Index1'] + '_' + x['Category'])
         .pivot('Index2', 'Category', 'Value')
         .rename_axis(columns=None).reset_index())
print(out)

# Output
   Index2   id_art   id_cat  name_art name_cat
0       1  Vintage  1293874       old      Sam
1       2  Retired  2030039  lacklust      Ben

How do I make index into a normal column in pandas?

You can try this:

# keep the index as a column
df = df.reset_index()

# drop it
df = df.reset_index(drop=True)

Add values to column pandas dataframe by index number

You can use isin for index, that you'd normally use with Series, it essentially creates an array of truth value, which you can pass to np.where with true and false values, assign the result as a column.

df['Is it valid?'] = np.where(df.index.isin(indexlist), 'Yes', 'No')

OUTPUT:

      Name Country Is it valid?
0     John      BR          Yes
1    Peter      BR          Yes
2     Paul      BR           No
3    James      CZ           No
4  Jonatan      CZ          Yes
5    Maria      DK           No

Method converting a dictionary stored in a Pandas DataFrame column into individual columns

IIUC, you can use:

data = pd.DataFrame(pd.read_hdf('test_data_20220720.h5'))
pd.DataFrame.from_records(data.rawdata_boxfit)

To merge new columns use:

data = pd.DataFrame(pd.read_hdf('test_data_20220720.h5'))
new = pd.DataFrame.from_records(data.rawdata_boxfit)
new.index = data.index
pd.concat([data, new], axis=1).drop('rawdata_boxfit', axis=1)

How to convert a pandas series of objects into a dataframe where each item becomes a column and the values in the rows

IIUC, this should work:

df = pd.DataFrame.from_records(sn)

find min and max of each column in pandas without min and max of index

just select from the second column of the result and save it in a new dataframe.

df_thd_funct_mode1_T.agg([min,max]).iloc[:,1:]

to save it to a new df:

new_df = df_thd_funct_mode1_T.agg([min,max]).iloc[:,1:]

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