I have a input list and dataframe as shown below.
[{"type": "linear",
"from": "2020-02-04T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "quadratic",
"from": "2020-02-03T20:00:00.000Z",
"to": "2020-02-10T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "polynomial",
"from": "2020-02-05T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}]
df:
Date t_factor
2020-02-01 5
2020-02-02 23
2020-02-03 14
2020-02-04 23
2020-02-05 23
2020-02-06 23
2020-02-07 30
2020-02-08 29
2020-02-09 100
2020-03-10 38
2020-03-11 38
2020-03-12 38
2020-03-13 70
2020-03-14 70
Step1: Sort the list based on the value of "from" key in dictionary
[
{"type": "quadratic",
"from": "2020-02-03T20:00:00.000Z",
"to": "2020-02-10T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "linear",
"from": "2020-02-04T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "polynomial",
"from": "2020-02-05T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}]
Step2:add a dictionary with value of "from" key as minimum date of df and "to" should be "from" date the first dictionary in the sorted list. "days" = 0, "coef":[0.1,0.1,0.1,0.1,0.1,0.1].
{"type": "df_first",
"from": "2020-02-01T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}
Step3:add a dictionary with value of "from" key as 7 days after minimum date of df and "to" should be one days after from
{"type": "df_mid",
"from": "2020-02-08T20:00:00.000Z",
"to": "2020-02-09T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}
Step4:add a dictionary with value of "from" key as maximum date of df and "to" should be same as well as "from".
{"type": "df_last",
"from": "2020-02-14T20:00:00.000Z",
"to": "2020-02-14T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}
Step 5: Sort all the dictionary based on "from" date.
Expected Output:
[{"type": "df_first",
"from": "2020-02-01T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "quadratic",
"from": "2020-02-03T20:00:00.000Z",
"to": "2020-02-10T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "linear",
"from": "2020-02-04T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "polynomial",
"from": "2020-02-05T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "df_mid",
"from": "2020-02-08T20:00:00.000Z",
"to": "2020-02-09T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "df_last",
"from": "2020-02-14T20:00:00.000Z",
"to": "2020-02-14T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}
]
Step 6:
Replace the "to" value of each dictionary with "from" value of next dictionary. "to" value of last dictionary be as it is.
Expected Final output:
[{"type": "df_first",
"from": "2020-02-01T20:00:00.000Z",
"to": "2020-02-03T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "quadratic",
"from": "2020-02-03T20:00:00.000Z",
"to": "2020-02-04T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "linear",
"from": "2020-02-04T20:00:00.000Z",
"to": "2020-02-05T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "polynomial",
"from": "2020-02-05T20:00:00.000Z",
"to": "2020-02-08T20:00:00.000Z",
"days":3,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "df_mid",
"from": "2020-02-08T20:00:00.000Z",
"to": "2020-02-14T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
},
{"type": "df_last",
"from": "2020-02-14T20:00:00.000Z",
"to": "2020-02-14T20:00:00.000Z",
"days":0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
}
]
Define a function add_dct that takes arguments as list of dictionaries lst with _type, _from and _to and appends a new dictionary to lst:
dmin, dmax = df['Date'].min(), df['Date'].max()
def add_dct(lst, _type, _from, _to):
lst.append({
'type': _type,
'from': _from if isinstance(_from, str) else _from.strftime("%Y-%m-%dT20:%M:%S.000Z"),
'to': _to if isinstance(_to, str) else _to.strftime("%Y-%m-%dT20:%M:%S.000Z"),
'days': 0,
"coef":[0.1,0.1,0.1,0.1,0.1,0.1]
})
Follow this steps as according to your predefined requirements:
# STEP 1
lst = sorted(lst, key=lambda d: pd.Timestamp(d['from']))
# STEP 2
add_dct(lst, 'df_first', dmin, lst[0]['from'])
# STEP 3
add_dct(lst, 'df_mid', dmin + pd.Timedelta(days=7), dmin + pd.Timedelta(days=8))
# STEP 4
add_dct(lst, 'df_last', dmax, dmax)
# STEP 5
lst = sorted(lst, key=lambda d: pd.Timestamp(d['from']))
Result:
[{'type': 'df_first',
'from': '2020-02-01T20:00:00.000Z',
'to': '2020-02-03T20:00:00.000Z',
'days': 0,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
{'type': 'quadratic',
'from': '2020-02-03T20:00:00.000Z',
'to': '2020-02-10T20:00:00.000Z',
'days': 3,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
{'type': 'linear',
'from': '2020-02-04T20:00:00.000Z',
'to': '2020-02-03T20:00:00.000Z',
'days': 3,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
{'type': 'polynomial',
'from': '2020-02-05T20:00:00.000Z',
'to': '2020-02-03T20:00:00.000Z',
'days': 3,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
{'type': 'df_mid',
'from': '2020-02-08T20:00:00.000Z',
'to': '2020-02-09T20:00:00.000Z',
'days': 0,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
{'type': 'df_last',
'from': '2020-03-14T20:00:00.000Z',
'to': '2020-03-14T20:00:00.000Z',
'days': 0,
'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]}]
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