maxframe.dataframe.DataFrame.mf.extract_kv#

DataFrame.mf.extract_kv(columns=None, kv_delim='=', item_delim=',', dtype='float', fill_value=None, errors='raise')#

Extract values in key-value represented columns into standalone columns. New column names will be the name of the key-value column followed by an underscore and the key.

Parameters:

  • columns (list, default None) – The key-value columns to be extracted.

  • kv_delim (str, default '=') – Delimiter between key and value.

  • item_delim (str, default ',') – Delimiter between key-value pairs.

  • dtype (str) – Type of value columns to generate.

  • fill_value (object, default None) – Default value for missing key-value pairs.

  • errors ({'ignore', 'raise'}, default 'raise') –

    • If ‘raise’, then invalid parsing will raise an exception.

    • If ‘ignore’, then invalid parsing will return the input.

Returns:

extracted data frame

Return type:

DataFrame

See also

DataFrame.mf.collect_kv

Examples

>>> import numpy as np
>>> import maxframe.dataframe as md

>>> df = md.DataFrame({"name": ["name1", "name2", "name3", "name4", "name5"],
...                    "kv": ["k1=1.0,k2=3.0,k5=10.0",
...                           "k2=3.0,k3=5.1",
...                           "k1=7.1,k7=8.2",
...                           "k2=1.2,k3=1.5",
...                           "k2=1.0,k9=1.1"]})
>>> df.execute()
   name   kv
0  name1  k1=1.0,k2=3.0,k5=10.0
1  name2  k2=3.0,k3=5.1
2  name3  k1=7.1,k7=8.2
3  name4  k2=1.2,k3=1.5
4  name5  k2=1.0,k9=1.1

The field names to be expanded are specified by columns kv_delim is to delimit the key and value and ‘=’ is default item_delim is to delimit the Key-Value pairs, ‘,’ is default The output field name is the original field name connect with the key by “_” fill_value is used to fill missing values, None is default

>>> df.mf.extract_kv(columns=['kv'], kv_delim='=', item_delim=',').execute()
   name   kv_k1   kv_k2   kv_k3   kv_k5   kv_k7   kv_k9
0  name1  1.0     3.0     NaN     10.0    NaN     NaN
1  name2  NaN     3.0     5.1     NaN     NaN     NaN
2  name3  7.1     NaN     NaN     NaN     8.2     NaN
3  name4  NaN     1.2     1.5     NaN     NaN     NaN
4  name5  NaN     1.0     NaN     NaN     NaN     1.1