module mlhelper.missing

Short summary

module pyensae.mlhelper.missing

Missing values and pandas.

source on GitHub

Functions

function truncated documentation
add_missing_indices After aggregation, it usually happens that the series is sparse. This function adds rows for missing time.

Documentation

Missing values and pandas.

source on GitHub

pyensae.mlhelper.missing.add_missing_indices(df, column, all_values, values=None, fillvalue=nan)[source]

After aggregation, it usually happens that the series is sparse. This function adds rows for missing time.

Parameters:
  • df – dataframe to extend
  • column – column with time
  • all_values – all the values we want
  • values – columns which contain the values, the others are considered as the keys
Returns:

new dataframe

Add missing values in one column.

<<<

import pandas
from pyensae.mlhelper import add_missing_indices
df = pandas.DataFrame([{"x": 3, "y": 4, "z": 1}, {"x": 5, "y": 6, "z": 2}])
df2 = add_missing_indices(df, "x", [3, 4, 5, 6])
print(df2)

>>>

       x  y  z
    0  3  4  1
    5  3  6  2
    2  4  4  1
    6  4  6  2
    1  5  6  2
    3  5  4  1
    4  6  4  1
    7  6  6  2

<<<

import pandas
from pyensae.mlhelper import add_missing_indices
df = pandas.DataFrame([{"x": 3, "y": 4, "z": 1}, {"x": 5, "y": 6, "z": 2}])
df2 = add_missing_indices(df, "x", values=["y"], all_values=[3, 4, 5, 6])
print(df2)

>>>

       x    y  z
    0  3  4.0  1
    5  3  NaN  2
    2  4  NaN  1
    6  4  NaN  2
    1  5  6.0  2
    3  5  NaN  1
    4  6  NaN  1
    7  6  NaN  2

source on GitHub