iode.Variables.from_frame

Variables.from_frame(df: DataFrame)[source]

Copy the pandas DataFrame df into the IODE Variables database. The variable names to copy are deduced from the index of the DataFrame. The column names must match the sub-periods of the current Variables sample. The column names can be of type string or int.

Parameters:
df: DataFrame

pandas DataFrame containing the variables to copy into the IODE Variables database.

Warning

IODE and pandas don’t use the same constant to represent NaN values. When loading a pandas DataFrame into the Variables database, the pandas NaN values (\(nan\)) are converted to IODE NaN values (\(NA\)).

See also

Variables.to_frame

Notes

The index of the passed DataFrame is sorted in alphabetical order before copying to IODE Variables database.

Examples

>>> from iode import variables
>>> import numpy as np
>>> import pandas as pd
>>> variables.clear()
>>> len(variables)
0

  1. Filling an empty Variables database from a pandas DataFrame

>>> # create the pandas DataFrame
>>> vars_names = [f"{region}_{code}" for region in ["VLA", "WAL", "BXL"] for code in ["00", "01", "02"]]
>>> periods_list = [f"{i}Y1" for i in range(1960, 1971)]
>>> nb_periods = len(periods_list)
>>> data = np.zeros((len(vars_names), nb_periods), dtype=float)
>>> for i in range(len(vars_names)):
...     for j in range(nb_periods):
...         data[i, j] = i * nb_periods + j
>>> df = pd.DataFrame(index=vars_names, columns=periods_list, data=data)
>>> # display the dataframe
>>> df
        1960Y1  1961Y1  1962Y1  1963Y1  ...  1967Y1  1968Y1  1969Y1  1970Y1
VLA_00     0.0     1.0     2.0     3.0  ...     7.0     8.0     9.0    10.0
VLA_01    11.0    12.0    13.0    14.0  ...    18.0    19.0    20.0    21.0
VLA_02    22.0    23.0    24.0    25.0  ...    29.0    30.0    31.0    32.0
WAL_00    33.0    34.0    35.0    36.0  ...    40.0    41.0    42.0    43.0
WAL_01    44.0    45.0    46.0    47.0  ...    51.0    52.0    53.0    54.0
WAL_02    55.0    56.0    57.0    58.0  ...    62.0    63.0    64.0    65.0
BXL_00    66.0    67.0    68.0    69.0  ...    73.0    74.0    75.0    76.0
BXL_01    77.0    78.0    79.0    80.0  ...    84.0    85.0    86.0    87.0
BXL_02    88.0    89.0    90.0    91.0  ...    95.0    96.0    97.0    98.0

[9 rows x 11 columns]

>>> # load into the IODE Variables database
>>> variables.from_frame(df)
>>> len(variables)
9
>>> variables.names
['BXL_00', 'BXL_01', 'BXL_02', 'VLA_00', 'VLA_01', 'VLA_02', 'WAL_00', 'WAL_01', 'WAL_02']
>>> variables.sample
Sample("1960Y1:1970Y1")
>>> variables["VLA_00"]
Workspace: Variables
nb variables: 1
filename: ws
sample: 1960Y1:1970Y1
mode: LEVEL

 name       1960Y1  1961Y1  1962Y1  ...  1968Y1  1969Y1  1970Y1
VLA_00        0.00    1.00    2.00  ...    8.00    9.00   10.00

>>> variables["BXL_02"]
Workspace: Variables
nb variables: 1
filename: ws
sample: 1960Y1:1970Y1
mode: LEVEL

 name       1960Y1  1961Y1  1962Y1  ...  1968Y1  1969Y1  1970Y1
BXL_02       88.00   89.00   90.00  ...   96.00   97.00   98.00

  1. Updating an existing Variables database from a pandas DataFrame

>>> # take a subset of the pandas DataFrame
>>> df = df.iloc[6:, 2:-2]
>>> # change values
>>> df += 3.0
>>> # add a new entry
>>> df.loc['BXL_03'] = [104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0]
>>> df
        1962Y1  1963Y1  ...  1967Y1  1968Y1
BXL_00    71.0    72.0  ...    76.0    77.0
BXL_01    82.0    83.0  ...    87.0    88.0
BXL_02    93.0    94.0  ...    98.0    99.0
BXL_03   104.0   105.0  ...   109.0   110.0
>>> # update the IODE Variables database
>>> variables.from_frame(df)
>>> len(variables)
10
>>> variables.names
['BXL_00', 'BXL_01', 'BXL_02', 'BXL_03', ..., 'WAL_00', 'WAL_01', 'WAL_02']
>>> # note that the new variable BXL_03 has been added with NA values 
>>> # for the periods present in the Variables sample but not in the DataFrame
>>> variables
Workspace: Variables
nb variables: 10
filename: ws
sample: 1960Y1:1970Y1
mode: LEVEL

 name      1960Y1  1961Y1  1962Y1  1963Y1  1964Y1  1965Y1  1966Y1  1967Y1  1968Y1  1969Y1  1970Y1        
BXL_00      66.00   67.00   71.00   72.00   73.00   74.00   75.00   76.00   77.00   75.00   76.00        
BXL_01      77.00   78.00   82.00   83.00   84.00   85.00   86.00   87.00   88.00   86.00   87.00        
BXL_02      88.00   89.00   93.00   94.00   95.00   96.00   97.00   98.00   99.00   97.00   98.00        
BXL_03         na      na  104.00  105.00  106.00  107.00  108.00  109.00  110.00      na      na        
VLA_00       0.00    1.00    2.00    3.00    4.00    5.00    6.00    7.00    8.00    9.00   10.00        
VLA_01      11.00   12.00   13.00   14.00   15.00   16.00   17.00   18.00   19.00   20.00   21.00        
VLA_02      22.00   23.00   24.00   25.00   26.00   27.00   28.00   29.00   30.00   31.00   32.00        
WAL_00      33.00   34.00   35.00   36.00   37.00   38.00   39.00   40.00   41.00   42.00   43.00        
WAL_01      44.00   45.00   46.00   47.00   48.00   49.00   50.00   51.00   52.00   53.00   54.00        
WAL_02      55.00   56.00   57.00   58.00   59.00   60.00   61.00   62.00   63.00   64.00   65.00