How to collapse/aggregate values into several columns of dicts - python
I want to collapse the rows of a dataframe such that each row represents an hour (the source data goes down to minute granularity). I don't want to lose the data, instead I want to create a dict so that the key of the dict is the minute (the source data doesn't go down to seconds) and then have the value be the value for that minute. See my brute force example at the bottom for what I'm really getting at.
I have data that looks something like this
import pandas as pd
from datetime import datetime, timedelta
import itertools
import random
from copy import deepcopy
fruits=['apple','banana','pear']
dts=[datetime(2022,1,1)+timedelta(minutes=x*random.randint(1,9)) for x in range(48)]
df=pd.DataFrame([x for x in itertools.product(fruits, dts)], columns=['fruit','dt'])
df['value1']=[random.randrange(0,100) for i in range(df.shape[0])]
df['value2']=[random.randrange(0,100) for i in range(df.shape[0])]
df['value3']=[random.randrange(0,100) for i in range(df.shape[0])]
# fruit dt value1 value2 value3
# 0 apple 2022-01-01 00:00:00 56 55 65
# 1 apple 2022-01-01 00:02:00 98 67 16
# 2 apple 2022-01-01 00:12:00 52 19 23
# 3 apple 2022-01-01 00:09:00 0 60 82
# 4 apple 2022-01-01 00:08:00 94 51 22
# ... ... ... ... ... ...
# 139 pear 2022-01-01 00:43:00 14 49 37
# 140 pear 2022-01-01 02:56:00 92 98 73
# 141 pear 2022-01-01 02:15:00 9 5 99
# 142 pear 2022-01-01 03:04:00 92 39 10
# 143 pear 2022-01-01 05:29:00 52 61 65
The best I can do using reasonable syntax is:
df['minute']=[x.minute for x in df.dt]
df['hour']=[x.hour+1 for x in df.dt]
df['date']=[x.replace(hour=0, minute=0) for x in df.dt]
def splitdict(x):
mydict={}
x=list(x)
for elem in x:
mydict[elem[0]]=elem[1]
return(mydict)
df.groupby(['fruit','date','hour']).apply(lambda x: splitdict(zip(df.minute,df.value1)))
Of course, this doesn't actually work (so I'll spare you the output). It returns the same dict for every row and it would only work for value1 (ie. I don't know how to get the results for value1, value2, value3 at once) I thought if I did a deepcopy that would resolve the repeating dict but it didn't so I took that out.
I did a horrendous for loop to brute force what I want. That looks like this:
#brute force
uniq=df.drop_duplicates(['fruit','date','hour'])
uniq=uniq[['fruit','date','hour']]
results=[]
for index, row in uniq.iterrows():
mydict=(df[(df['date']==row['date']) & (df['hour']==row['hour']) & (df['fruit']==row['fruit'])].loc[:,['minute','value1','value2','value3']]).to_dict('records')
value1dict={mydict[i]['minute']:mydict[i]['value1'] for i in range(len(mydict))}
value2dict={mydict[i]['minute']:mydict[i]['value2'] for i in range(len(mydict))}
value3dict={mydict[i]['minute']:mydict[i]['value3'] for i in range(len(mydict))}
results.append(pd.DataFrame({'date':row['date'], 'hour':row['hour'], 'fruit':row['fruit'] , 'value1':[value1dict], 'value2':[value2dict], 'value3':[value3dict]}))
results=pd.concat(results)
# date hour fruit value1 value2 value3
# 0 2022-01-01 1 apple {0: 56, 2: 98, 12: 52, 9: 0, 8: 94, 30: 90, 48: 92, 21: 16, 45: 9, 40: 58, 24: 31, 13: 45, 28: 86, 57: 47, 56: 66, 34: 69, 39: 12, 41: 92, 43: 12} {0: 55, 2: 67, 12: 19, 9: 60, 8: 51, 30: 95, 48: 62, 21: 97, 45: 1, 40: 89, 24: 52, 13: 5, 28: 67, 57: 16, 56: 13, 34: 54, 39: 86, 41: 45, 43: 50} {0: 65, 2: 16, 12: 23, 9: 82, 8: 22, 30: 76, 48: 98, 21: 23, 45: 14, 40: 87, 24: 45, 13: 87, 28: 29, 57: 75, 56: 26, 34: 25, 39: 70, 41: 97, 43: 89}
# 0 2022-01-01 2 apple {12: 31, 39: 42, 42: 87, 20: 86, 45: 62, 28: 86, 55: 59, 44: 83, 0: 26, 2: 97, 36: 55, 24: 44} {12: 74, 39: 55, 42: 16, 20: 12, 45: 92, 28: 2, 55: 98, 44: 44, 0: 63, 2: 54, 36: 76, 24: 55} {12: 97, 39: 81, 42: 76, 20: 71, 45: 26, 28: 56, 55: 61, 44: 93, 0: 90, 2: 87, 36: 28, 24: 52}
# 0 2022-01-01 3 apple {15: 72, 8: 84, 24: 97, 0: 54, 30: 10, 12: 50, 55: 51, 56: 31} {15: 30, 8: 42, 24: 96, 0: 76, 30: 58, 12: 44, 55: 82, 56: 57} {15: 41, 8: 11, 24: 40, 0: 89, 30: 22, 12: 51, 55: 57, 56: 60}
# 0 2022-01-01 5 apple {3: 14, 56: 79, 0: 25} {3: 3, 56: 61, 0: 49} {3: 2, 56: 29, 0: 32}
# 0 2022-01-01 4 apple {52: 53, 0: 50, 4: 53} {52: 32, 0: 64, 4: 26} {52: 88, 0: 28, 4: 59}
# 0 2022-01-01 6 apple {42: 40, 29: 52} {42: 30, 29: 82} {42: 50, 29: 33}
# 0 2022-01-01 1 banana {0: 6, 2: 10, 12: 43, 9: 77, 8: 19, 30: 59, 48: 67, 21: 17, 45: 92, 40: 69, 24: 25, 13: 86, 28: 81, 57: 72, 56: 35, 34: 22, 39: 61, 41: 8, 43: 56} {0: 63, 2: 92, 12: 49, 9: 22, 8: 2, 30: 92, 48: 96, 21: 21, 45: 62, 40: 23, 24: 77, 13: 41, 28: 64, 57: 49, 56: 30, 34: 59, 39: 63, 41: 54, 43: 85} {0: 27, 2: 83, 12: 35, 9: 37, 8: 70, 30: 94, 48: 16, 21: 19, 45: 71, 40: 5, 24: 26, 13: 91, 28: 16, 57: 42, 56: 8, 34: 31, 39: 93, 41: 57, 43: 65}
# 0 2022-01-01 2 banana {12: 0, 39: 53, 42: 13, 20: 91, 45: 88, 28: 66, 55: 46, 44: 24, 0: 41, 2: 32, 36: 69, 24: 11} {12: 83, 39: 21, 42: 41, 20: 81, 45: 79, 28: 37, 55: 28, 44: 2, 0: 51, 2: 87, 36: 63, 24: 82} {12: 6, 39: 57, 42: 18, 20: 3, 45: 74, 28: 58, 55: 46, 44: 25, 0: 45, 2: 96, 36: 37, 24: 14}
# 0 2022-01-01 3 banana {15: 10, 8: 14, 24: 60, 0: 48, 30: 82, 12: 70, 55: 39, 56: 4} {15: 81, 8: 55, 24: 8, 0: 64, 30: 72, 12: 45, 55: 79, 56: 45} {15: 95, 8: 90, 24: 36, 0: 47, 30: 88, 12: 12, 55: 86, 56: 75}
# 0 2022-01-01 5 banana {3: 47, 56: 39, 0: 60} {3: 60, 56: 15, 0: 72} {3: 48, 56: 86, 0: 16}
# 0 2022-01-01 4 banana {52: 49, 0: 30, 4: 86} {52: 39, 0: 85, 4: 5} {52: 64, 0: 22, 4: 96}
# 0 2022-01-01 6 banana {42: 2, 29: 26} {42: 15, 29: 54} {42: 61, 29: 58}
# 0 2022-01-01 1 pear {0: 39, 2: 55, 12: 25, 9: 98, 8: 14, 30: 82, 48: 59, 21: 77, 45: 8, 40: 75, 24: 19, 13: 92, 28: 39, 57: 63, 56: 95, 34: 77, 39: 77, 41: 41, 43: 14} {0: 54, 2: 11, 12: 63, 9: 12, 8: 38, 30: 34, 48: 96, 21: 27, 45: 19, 40: 87, 24: 83, 13: 28, 28: 22, 57: 25, 56: 38, 34: 66, 39: 80, 41: 80, 43: 49} {0: 98, 2: 71, 12: 97, 9: 54, 8: 70, 30: 22, 48: 31, 21: 4, 45: 47, 40: 42, 24: 28, 13: 68, 28: 65, 57: 73, 56: 32, 34: 1, 39: 73, 41: 39, 43: 37}
# 0 2022-01-01 2 pear {12: 93, 39: 57, 42: 12, 20: 98, 45: 69, 28: 60, 55: 77, 44: 16, 0: 96, 2: 16, 36: 76, 24: 15} {12: 23, 39: 63, 42: 59, 20: 15, 45: 66, 28: 50, 55: 18, 44: 87, 0: 33, 2: 15, 36: 9, 24: 90} {12: 80, 39: 50, 42: 98, 20: 12, 45: 54, 28: 90, 55: 67, 44: 37, 0: 86, 2: 2, 36: 51, 24: 64}
# 0 2022-01-01 3 pear {15: 9, 8: 71, 24: 3, 0: 94, 30: 53, 12: 90, 55: 28, 56: 92} {15: 5, 8: 85, 24: 77, 0: 53, 30: 26, 12: 62, 55: 2, 56: 98} {15: 99, 8: 4, 24: 14, 0: 86, 30: 50, 12: 17, 55: 70, 56: 73}
# 0 2022-01-01 5 pear {3: 57, 56: 2, 0: 65} {3: 32, 56: 16, 0: 90} {3: 88, 56: 74, 0: 80}
# 0 2022-01-01 4 pear {52: 23, 0: 32, 4: 92} {52: 4, 0: 93, 4: 39} {52: 30, 0: 97, 4: 10}
# 0 2022-01-01 6 pear {42: 43, 29: 52} {42: 63, 29: 61} {42: 80, 29: 65}
IIUC
cols_to_dict = ['value1', 'value2', 'value3']
out = df.assign(date=df['dt'].dt.date, hour=df['dt'].dt.hour, minute=df['dt'].dt.minute) \
.set_index('minute').groupby(['date', 'hour', 'fruit'])[cols_to_dict] \
.agg(dict).sort_index(level=['date', 'fruit', 'hour']).reset_index()
Output:
>>> out
date hour fruit value1 value2 value3
0 2022-01-01 0 apple {0: 67, 9: 82, 6: 33, 21: 74, 16: 99, 20: 82, ... {0: 46, 9: 47, 6: 57, 21: 21, 16: 8, 20: 96, 1... {0: 25, 9: 42, 6: 1, 21: 99, 16: 63, 20: 47, 1...
1 2022-01-01 1 apple {3: 30, 0: 36, 5: 81, 24: [51, 57, 59], 25: 77... {3: 40, 0: 32, 5: 14, 24: [18, 49, 58], 25: 60... {3: 83, 0: 80, 5: 17, 24: [37, 24, 34], 25: 16...
2 2022-01-01 2 apple {0: 49, 24: [64, 26], 40: 6, 18: 48, 4: 11, 16... {0: 77, 24: [53, 77], 40: 80, 18: 11, 4: 76, 1... {0: 0, 24: [62, 75], 40: 44, 18: 13, 4: 56, 16...
3 2022-01-01 3 apple {20: [88, 88], 28: 94, 54: 48, 25: 34} {20: [68, 34], 28: 55, 54: 15, 25: 46} {20: [16, 49], 28: 50, 54: 11, 25: 26}
4 2022-01-01 4 apple {36: 91} {36: 86} {36: 95}
5 2022-01-01 5 apple {33: 21, 36: 70, 8: 89} {33: 34, 36: 66, 8: 69} {33: 39, 36: 38, 8: 92}
6 2022-01-01 6 apple {45: 87} {45: 80} {45: 24}
7 2022-01-01 0 banana {0: 67, 9: 67, 6: 64, 21: 80, 16: 29, 20: 85, ... {0: 30, 9: 33, 6: 4, 21: 64, 16: 29, 20: 59, 1... {0: 92, 9: 37, 6: 93, 21: 40, 16: 49, 20: 97, ...
8 2022-01-01 1 banana {3: 12, 0: 73, 5: 6, 24: [19, 81, 44], 25: 11,... {3: 41, 0: 22, 5: 86, 24: [68, 41, 7], 25: 25,... {3: 94, 0: 52, 5: 15, 24: [19, 59, 12], 25: 2,...
9 2022-01-01 2 banana {0: 49, 24: [36, 87], 40: 35, 18: 26, 4: 21, 1... {0: 96, 24: [30, 45], 40: 86, 18: 33, 4: 2, 16... {0: 53, 24: [39, 22], 40: 84, 18: 7, 4: 47, 16...
10 2022-01-01 3 banana {20: [45, 27], 28: 58, 54: 96, 25: 90} {20: [14, 22], 28: 49, 54: 14, 25: 90} {20: [94, 50], 28: 19, 54: 26, 25: 65}
11 2022-01-01 4 banana {36: 64} {36: 62} {36: 43}
12 2022-01-01 5 banana {33: 83, 36: 4, 8: 34} {33: 13, 36: 36, 8: 37} {33: 2, 36: 63, 8: 94}
13 2022-01-01 6 banana {45: 22} {45: 2} {45: 0}
14 2022-01-01 0 pear {0: 55, 9: 29, 6: 30, 21: 57, 16: 37, 20: 63, ... {0: 74, 9: 38, 6: 18, 21: 47, 16: 47, 20: 34, ... {0: 25, 9: 75, 6: 36, 21: 60, 16: 94, 20: 68, ...
15 2022-01-01 1 pear {3: 20, 0: 22, 5: 1, 24: [94, 27, 44], 25: 73,... {3: 59, 0: 50, 5: 7, 24: [34, 15, 28], 25: 24,... {3: 90, 0: 71, 5: 75, 24: [4, 4, 63], 25: 73, ...
16 2022-01-01 2 pear {0: 42, 24: [83, 98], 40: 83, 18: 34, 4: 58, 1... {0: 55, 24: [13, 50], 40: 39, 18: 37, 4: 68, 1... {0: 69, 24: [80, 49], 40: 80, 18: 82, 4: 13, 1...
17 2022-01-01 3 pear {20: [33, 50], 28: 47, 54: 16, 25: 0} {20: [28, 26], 28: 74, 54: 66, 25: 13} {20: [40, 67], 28: 88, 54: 96, 25: 4}
18 2022-01-01 4 pear {36: 46} {36: 28} {36: 97}
19 2022-01-01 5 pear {33: 27, 36: 23, 8: 57} {33: 71, 36: 82, 8: 57} {33: 23, 36: 88, 8: 91}
20 2022-01-01 6 pear {45: 83} {45: 45} {45: 3}
Related
Updated StatsForecast Library shows error 'forecasts' is not defined in Python
I was trying to replicate this code for stat forecasting in python, I came across an odd error "name 'forecasts' is not defined" which is quite strange as I was able to replicate the code without any errors before.
I believe this was resolved in the latest update of this library StatsForecast but I still run across to the same error. Can you please help me out here.
The code I am replicating is from this : https://towardsdatascience.com/time-series-forecasting-with-statistical-models-f08dcd1d24d1
A similar question was earlier asked for the same error, and the solution was updated but this error till comes up after the new solution as well, attached is the link to the question: Error in Data frame definition while Multiple TS Stat Forecasting in Python
import random
from itertools import product
from IPython.display import display, Markdown
from multiprocessing import cpu_count
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from statsforecast import StatsForecast
from nixtlats.data.datasets.m4 import M4, M4Info
from statsforecast.models import CrostonClassic, CrostonSBA, CrostonOptimized, ADIDA, IMAPA, TSB
from statsforecast.models import SimpleExponentialSmoothing, SimpleExponentialSmoothingOptimized, SeasonalExponentialSmoothing, SeasonalExponentialSmoothingOptimized, Holt, HoltWinters
from statsforecast.models import HistoricAverage, Naive, RandomWalkWithDrift, SeasonalNaive, WindowAverage, SeasonalWindowAverage
from statsforecast.models import MSTL
from statsforecast.models import Theta, OptimizedTheta, DynamicTheta, DynamicOptimizedTheta
from statsforecast.models import AutoARIMA, AutoETS, AutoCES, AutoTheta
df = pd.read_excel ('C:/X/X/X/Data.xlsx',sheet_name='Transpose')
df.rename(columns = {'Row Labels':'Key'}, inplace=True)
df['Key'] = df['Key'].astype(str)
df = pd.melt(df,id_vars='Key',value_vars=list(df.columns[1:]),var_name ='ds')
df.columns = df.columns.str.replace('Key', 'unique_id')
df.columns = df.columns.str.replace('value', 'y')
df["ds"] = pd.to_datetime(df["ds"],format='%Y-%m-%d')
df=df[["ds","unique_id","y"]]
df['unique_id'] = df['unique_id'].astype('object')
df = df.set_index('unique_id')
df.reset_index()
seasonality = 30 #Monthly data
models = [
ADIDA,
CrostonClassic(),
CrostonSBA(),
CrostonOptimized(),
IMAPA,
(TSB,0.3,0.2),
MSTL,
Theta,
OptimizedTheta,
DynamicTheta,
DynamicOptimizedTheta,
AutoARIMA,
AutoETS,
AutoCES,
AutoTheta,
HistoricAverage,
Naive,
RandomWalkWithDrift,
(SeasonalNaive, seasonality),
(SeasonalExponentialSmoothing, seasonality, 0.2),
(SeasonalWindowAverage, seasonality, 2 * seasonality),
(WindowAverage, 2 * seasonality)
]
fcst = StatsForecast(df=df, models=models, freq='MS', n_jobs=cpu_count())
%time forecasts = fcst.forecast(9)
forecasts.reset_index()
forecasts = forecasts.round(0)
forecasts.to_excel("C:/X/X/X/Forecast_Output.xlsx",sheet_name='Sheet1')
The dataset I am working with is given below:
{'Row Labels': {0: 'XYZ-912750', 1: 'XYZ-461356', 2: 'XYZ-150591', 3: 'XYZ-627885', 4: 'XYZ-582638', 5: 'XYZ-631691', 6: 'XYZ-409952', 7: 'XYZ-245245', 8: 'XYZ-230662', 9: 'XYZ-533388', 10: 'XYZ-248225', 11: 'XYZ-582912', 12: 'XYZ-486079', 13: 'XYZ-867685', 14: 'XYZ-873555', 15: 'XYZ-375397', 16: 'XYZ-428066', 17: 'XYZ-774244', 18: 'XYZ-602796', 19: 'XYZ-267306', 20: 'XYZ-576156', 21: 'XYZ-775994', 22: 'XYZ-226742', 23: 'XYZ-641711', 24: 'XYZ-928543', 25: 'XYZ-217200', 26: 'XYZ-971921', 27: 'XYZ-141388', 28: 'XYZ-848360', 29: 'XYZ-864999', 30: 'XYZ-821384', 31: 'XYZ-516339', 32: 'XYZ-462488', 33: 'XYZ-140964', 34: 'XYZ-225559', 35: 'XYZ-916534', 36: 'XYZ-389683', 37: 'XYZ-247803', 38: 'XYZ-718639', 39: 'XYZ-512944', 40: 'XYZ-727601', 41: 'XYZ-315757', 42: 'XYZ-764867', 43: 'XYZ-918344', 44: 'XYZ-430939', 45: 'XYZ-204784', 46: 'XYZ-415285', 47: 'XYZ-272089', 48: 'XYZ-812045', 49: 'XYZ-889257', 50: 'XYZ-275863', 51: 'XYZ-519930', 52: 'XYZ-102141', 53: 'XYZ-324473', 54: 'XYZ-999148', 55: 'XYZ-514915', 56: 'XYZ-932751', 57: 'XYZ-669878', 58: 'XYZ-233459', 59: 'XYZ-289984', 60: 'XYZ-150061', 61: 'XYZ-355028', 62: 'XYZ-881803', 63: 'XYZ-721426', 64: 'XYZ-522174', 65: 'XYZ-790172', 66: 'XYZ-744677', 67: 'XYZ-617017', 68: 'XYZ-982812', 69: 'XYZ-940695', 70: 'XYZ-119041', 71: 'XYZ-313844', 72: 'XYZ-868117', 73: 'XYZ-791717', 74: 'XYZ-100742', 75: 'XYZ-259687', 76: 'XYZ-688842', 77: 'XYZ-247326', 78: 'XYZ-360939', 79: 'XYZ-185017', 80: 'XYZ-244773', 81: 'XYZ-289058', 82: 'XYZ-477846', 83: 'XYZ-305072', 84: 'XYZ-828236', 85: 'XYZ-668927', 86: 'XYZ-616913', 87: 'XYZ-874876', 88: 'XYZ-371693', 89: 'XYZ-951238', 90: 'XYZ-371675', 91: 'XYZ-736997', 92: 'XYZ-922244', 93: 'XYZ-883225', 94: 'XYZ-267555', 95: 'XYZ-704013', 96: 'XYZ-874917', 97: 'XYZ-567402', 98: 'XYZ-167338', 99: 'XYZ-592671', 100: 'XYZ-130168', 101: 'XYZ-492522', 102: 'XYZ-696211', 103: 'XYZ-310469', 104: 'XYZ-973277', 105: 'XYZ-841356', 106: 'XYZ-389440', 107: 'XYZ-613876', 108: 'XYZ-662850', 109: 'XYZ-800625', 110: 'XYZ-500125', 111: 'XYZ-539949', 112: 'XYZ-576121', 113: 'XYZ-339006', 114: 'XYZ-247314', 115: 'XYZ-129049', 116: 'XYZ-980653', 117: 'XYZ-678520', 118: 'XYZ-584841', 119: 'XYZ-396755', 120: 'XYZ-409502', 121: 'XYZ-824561', 122: 'XYZ-825996', 123: 'XYZ-820540', 124: 'XYZ-264710', 125: 'XYZ-241176', 126: 'XYZ-491386', 127: 'XYZ-914132', 128: 'XYZ-496194', 129: 'XYZ-941615', 130: 'XYZ-765328', 131: 'XYZ-540602', 132: 'XYZ-222660', 133: 'XYZ-324367', 134: 'XYZ-583764', 135: 'XYZ-248478', 136: 'XYZ-379180', 137: 'XYZ-628462', 138: 'XYZ-454262'}, '2021-03-01': {0: 0, 1: 951, 2: 0, 3: 0, 4: 13, 5: 0, 6: 0, 7: 0, 8: 487, 9: 501, 10: 0, 11: 0, 12: 0, 13: 0, 14: 715, 15: 726, 16: 235, 17: 340, 18: 0, 19: 0, 20: 0, 21: 960, 22: 127, 23: 92, 24: 0, 25: 0, 26: 170, 27: 0, 28: 0, 29: 0, 30: 0, 31: 133, 32: 0, 33: 0, 34: 105, 35: 168, 36: 0, 37: 500, 38: 0, 39: 0, 40: 61, 41: 0, 42: 212, 43: 101, 44: 0, 45: 0, 46: 0, 47: 83, 48: 185, 49: 0, 50: 131, 51: 67, 52: 0, 53: 141, 54: 0, 55: 140, 56: 0, 57: 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25, 48: 93, 49: 0, 50: 42, 51: 80, 52: 20, 53: 105, 54: 52, 55: 70, 56: 46, 57: 31, 58: 86, 59: 39, 60: 32, 61: 33, 62: 103, 63: 16, 64: 49, 65: 24, 66: 22, 67: 0, 68: 161, 69: 78, 70: 31, 71: 36, 72: 28, 73: 73, 74: 57, 75: 21, 76: 30, 77: 39, 78: 22, 79: 70, 80: 24, 81: 55, 82: 134, 83: 25, 84: 16, 85: 28, 86: 24, 87: 28, 88: 31, 89: 17, 90: 60, 91: 30, 92: 32, 93: 49, 94: 20, 95: 13, 96: 12, 97: 31, 98: 20, 99: 25, 100: 21, 101: 33, 102: 29, 103: 36, 104: 23, 105: 26, 106: 26, 107: 31, 108: 30, 109: 15, 110: 22, 111: 20, 112: 32, 113: 27, 114: 39, 115: 18, 116: 40, 117: 31, 118: 21, 119: 24, 120: 52, 121: 22, 122: 62, 123: 37, 124: 16, 125: 19, 126: 17, 127: 23, 128: 17, 129: 15, 130: 0, 131: 22, 132: 32, 133: 24, 134: 20, 135: 21, 136: 13, 137: 23, 138: 25}, '2022-06-01': {0: 613, 1: 1944, 2: 1826, 3: 494, 4: 0, 5: 244, 6: 928, 7: 798, 8: 219, 9: 1529, 10: 1029, 11: 526, 12: 122, 13: 195, 14: 173, 15: 1261, 16: 87, 17: 243, 18: 1179, 19: 217, 20: 464, 21: 952, 22: 353, 23: 148, 24: 166, 25: 187, 26: 134, 27: 124, 28: 321, 29: 221, 30: 193, 31: 224, 32: 75, 33: 0, 34: 277, 35: 77, 36: 253, 37: 174, 38: 343, 39: 283, 40: 73, 41: 295, 42: 108, 43: 138, 44: 102, 45: 1364, 46: 467, 47: 28, 48: 87, 49: 16, 50: 145, 51: 88, 52: 128, 53: 60, 54: 80, 55: 81, 56: 40, 57: 206, 58: 61, 59: 166, 60: 144, 61: 71, 62: 78, 63: 39, 64: 331, 65: 116, 66: 25, 67: 13, 68: 62, 69: 37, 70: 24, 71: 311, 72: 106, 73: 50, 74: 257, 75: 22, 76: 56, 77: 128, 78: 100, 79: 55, 80: 139, 81: 70, 82: 140, 83: 20, 84: 53, 85: 33, 86: 38, 87: 167, 88: 218, 89: 20, 90: 34, 91: 19, 92: 25, 93: 199, 94: 122, 95: 24, 96: 28, 97: 36, 98: 69, 99: 146, 100: 33, 101: 14, 102: 21, 103: 27, 104: 28, 105: 78, 106: 62, 107: 30, 108: 47, 109: 20, 110: 78, 111: 48, 112: 35, 113: 21, 114: 17, 115: 49, 116: 61, 117: 92, 118: 26, 119: 16, 120: 47, 121: 36, 122: 54, 123: 43, 124: 23, 125: 40, 126: 22, 127: 121, 128: 145, 129: 12, 130: 18, 131: 31, 132: 31, 133: 17, 134: 23, 135: 23, 136: 19, 137: 24, 138: 24}, '2022-07-01': {0: 349, 1: 283, 2: 163, 3: 318, 4: 67, 5: 328, 6: 121, 7: 96, 8: 205, 9: 219, 10: 89, 11: 60, 12: 153, 13: 68, 14: 135, 15: 181, 16: 53, 17: 94, 18: 65, 19: 96, 20: 67, 21: 57, 22: 67, 23: 59, 24: 134, 25: 94, 26: 78, 27: 142, 28: 33, 29: 29, 30: 45, 31: 64, 32: 65, 33: 76, 34: 81, 35: 55, 36: 44, 37: 83, 38: 15, 39: 46, 40: 84, 41: 45, 42: 56, 43: 54, 44: 50, 45: 48, 46: 90, 47: 17, 48: 56, 49: 27, 50: 66, 51: 37, 52: 34, 53: 63, 54: 58, 55: 27, 56: 45, 57: 74, 58: 51, 59: 61, 60: 80, 61: 45, 62: 65, 63: 34, 64: 27, 65: 30, 66: 18, 67: 35, 68: 47, 69: 31, 70: 24, 71: 40, 72: 18, 73: 30, 74: 44, 75: 26, 76: 31, 77: 32, 78: 29, 79: 29, 80: 45, 81: 14, 82: 54, 83: 31, 84: 37, 85: 24, 86: 32, 87: 20, 88: 40, 89: 32, 90: 22, 91: 17, 92: 30, 93: 29, 94: 20, 95: 52, 96: 34, 97: 25, 98: 26, 99: 28, 100: 72, 101: 17, 102: 15, 103: 22, 104: 28, 105: 24, 106: 28, 107: 19, 108: 25, 109: 25, 110: 38, 111: 19, 112: 27, 113: 26, 114: 15, 115: 22, 116: 28, 117: 24, 118: 33, 119: 13, 120: 57, 121: 40, 122: 22, 123: 14, 124: 18, 125: 23, 126: 20, 127: 38, 128: 20, 129: 14, 130: 36, 131: 24, 132: 18, 133: 39, 134: 14, 135: 40, 136: 16, 137: 21, 138: 13}, '2022-08-01': {0: 857, 1: 500, 2: 362, 3: 334, 4: 308, 5: 296, 6: 289, 7: 266, 8: 244, 9: 223, 10: 206, 11: 192, 12: 180, 13: 169, 14: 160, 15: 159, 16: 140, 17: 134, 18: 134, 19: 128, 20: 127, 21: 126, 22: 123, 23: 116, 24: 112, 25: 111, 26: 108, 27: 102, 28: 99, 29: 94, 30: 94, 31: 89, 32: 88, 33: 88, 34: 87, 35: 85, 36: 83, 37: 79, 38: 78, 39: 77, 40: 77, 41: 77, 42: 76, 43: 75, 44: 75, 45: 74, 46: 72, 47: 65, 48: 65, 49: 65, 50: 64, 51: 64, 52: 64, 53: 62, 54: 62, 55: 61, 56: 61, 57: 61, 58: 60, 59: 60, 60: 58, 61: 55, 62: 54, 63: 54, 64: 54, 65: 54, 66: 53, 67: 53, 68: 52, 69: 50, 70: 49, 71: 49, 72: 49, 73: 48, 74: 48, 75: 48, 76: 47, 77: 47, 78: 46, 79: 44, 80: 44, 81: 43, 82: 43, 83: 43, 84: 42, 85: 42, 86: 41, 87: 41, 88: 41, 89: 40, 90: 39, 91: 39, 92: 39, 93: 39, 94: 38, 95: 37, 96: 37, 97: 36, 98: 36, 99: 36, 100: 36, 101: 35, 102: 35, 103: 35, 104: 35, 105: 35, 106: 35, 107: 34, 108: 34, 109: 34, 110: 32, 111: 32, 112: 32, 113: 32, 114: 31, 115: 31, 116: 30, 117: 30, 118: 30, 119: 30, 120: 29, 121: 29, 122: 28, 123: 28, 124: 28, 125: 28, 126: 28, 127: 28, 128: 28, 129: 28, 130: 28, 131: 27, 132: 27, 133: 27, 134: 27, 135: 27, 136: 27, 137: 27, 138: 26}}
You have to instantiate the models since they are classes. The code would be, from statsforecast import StatsForecast from statsforecast.models import CrostonClassic, CrostonSBA, CrostonOptimized, ADIDA, IMAPA, TSB from statsforecast.models import SimpleExponentialSmoothing, SimpleExponentialSmoothingOptimized, SeasonalExponentialSmoothing, SeasonalExponentialSmoothingOptimized, Holt, HoltWinters from statsforecast.models import HistoricAverage, Naive, RandomWalkWithDrift, SeasonalNaive, WindowAverage, SeasonalWindowAverage from statsforecast.models import MSTL from statsforecast.models import Theta, OptimizedTheta, DynamicTheta, DynamicOptimizedTheta from statsforecast.models import AutoARIMA, AutoETS, AutoCES, AutoTheta seasonality = 12 #Monthly data models = [ ADIDA(), CrostonClassic(), CrostonSBA(), CrostonOptimized(), IMAPA(), TSB(0.3,0.2), Theta(season_length=seasonality), OptimizedTheta(season_length=seasonality), DynamicTheta(season_length=seasonality), DynamicOptimizedTheta(season_length=seasonality), AutoARIMA(season_length=seasonality), AutoCES(season_length=seasonality), AutoTheta(season_length=seasonality), HistoricAverage(), Naive(), RandomWalkWithDrift(), SeasonalNaive(season_length=seasonality), SeasonalExponentialSmoothing(season_length=seasonality, alpha=0.2), ] fcst = StatsForecast(df=df, models=models, freq='MS', n_jobs=-1, fallback_model=SeasonalNaive(season_length=seasonality)) %time forecasts = fcst.forecast(9) forecasts.reset_index() forecasts = forecasts.round(0) Here's a colab link fixing the error: https://colab.research.google.com/drive/1vwIImCoKzGvePbgFKidauV8sXimAvO48?usp=sharing
How to create a range in a dictionairy
I have following dictionairy:
total_working_years_dict = dict(df.TotalWorkingYears.value_counts())
total_working_years_dict
{10: 202,
6: 125,
8: 103,
9: 96,
5: 88,
1: 81,
7: 81,
4: 63,
12: 48,
3: 42,
15: 40,
16: 37,
13: 36,
11: 36,
21: 34,
17: 33,
14: 31,
2: 31,
20: 30,
18: 27,
19: 22,
23: 22,
22: 21,
24: 18,
25: 14,
28: 14,
26: 14,
0: 11,
29: 10,
31: 9,
32: 9,
27: 7,
30: 7,
33: 7,
36: 6,
34: 5,
37: 4,
35: 3,
40: 2,
38: 1}
The keys are working years and values are numbers of employees which have such experience. I would like to transform my dictionairy so that total working yeras are given in ranges (0,6), (6,11) etc.
Do you have any idea how to do that ?
Let's start from your dict as a Series: s = pd.Series(total_working_years_dict) you can use pandas.cut to form your groups: s.index = pd.cut(s.index, bins=range(0,100,6)) output: (6.0, 12.0] 202 (0.0, 6.0] 125 (6.0, 12.0] 103 (6.0, 12.0] 96 (0.0, 6.0] 88 ... (30.0, 36.0] 3 (36.0, 42.0] 2 (36.0, 42.0] 1 dtype: int64 NB. if you now want to aggregate the counts per group, it would be more efficient to proceed to the pandas.cut operation before your initial value_counts. Also, I don't get the point of converting the Series to dict if you need to further process it.
Create a dictionary from dataframe column which has more than one value in its cell
I have a dataframe like this,
I want to create a dictionary from this to remap a column in another data frame ( if you look at 330th row it has 524 and 545. I want to assign a single value(330) in another dataframe)
So i used this code to create a dictorionary.
di = new2.T.to_dict('list')
But the dictionary i get is this,
{0: ['-1'],
1: ['187'],
2: ['212'],
3: ['30'],
4: ['209'],
5: ['213'],
6: ['214'],
7: ['238'],
8: ['544'],
9: ['557'],
10: ['317'],
11: ['516'],
12: ['571'],
13: ['184, 549'],
14: ['64'],
15: ['43'],
16: ['584'],
17: ['185'],
18: ['190'],
19: ['218'],
20: ['174'],
21: ['550'],
22: ['138'],
23: ['1'],
24: ['311'],
25: ['576'],
26: ['500'],
27: ['208, 241'],
28: ['16'],
29: ['327'],
30: ['3, 34, 50'],
31: ['332'],
32: ['520'],
33: ['491'],
34: ['535'],
35: ['523'],
36: ['119'],
37: ['482'],
38: ['574'],
39: ['165'],
40: ['370'],
41: ['51, 62, 73, 87, 101, 120, 199, 240, 304, 360, 506'],
And when i invert it using this code,
{value: key for key, values in di.items() for value in values}
It becomes this,
{'-1': 0,
'187': 1,
'212': 2,
'30': 3,
'209': 4,
'213': 5,
'214': 6,
'238': 7,
'544': 8,
'557': 9,
'317': 10,
'516': 11,
'571': 12,
'184, 549': 13,
'64': 14,
'43': 15,
'584': 16,
'185': 17,
'190': 18,
'218': 19,
'174': 20,
'550': 21,
'138': 22,
'1': 23,
'311': 24,
'576': 25,
'500': 26,
'208, 241': 27,
'16': 28,
'327': 29,
'3, 34, 50': 30,
'332': 31,
'520': 32,
'491': 33,
'535': 34,
'523': 35,
'119': 36,
'482': 37,
'574': 38,
'165': 39,
'370': 40,
'51, 62, 73, 87, 101, 120, 199, 240, 304, 360, 506': 41,
'525': 42,
But I want is to map them individually such as,
184: 13,
549: 13,
instead of this,
'184, 549': 13,
and use .map() function to map it using the dictionary
The problem is that you have string inside a list, just split the string:
di = {
27: ['208, 241'],
28: ['16'],
29: ['327'],
30: ['3, 34, 50'],
31: ['332'],
32: ['520'],
33: ['491']
}
result = {value: key for key, values in di.items() for value in values[0].split(', ')}
print(result)
Output
{'208': 27, '241': 27, '16': 28, '327': 29, '3': 30, '34': 30, '50': 30, '332': 31, '520': 32, '491': 33}
Note that I used a small fraction of di as an example, this can be applied to the whole dictionary.
Returning the three maximal values in a dictionary
I have the following dictionary:
'{0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49, 9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408, 16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}'
And for this dictionary I want write a function that returns the three key-value pairs that have the highest values (So in this case key 18, 19, 20).
I came up with the following:
cachedict = nr_of_objects_per_century() #Dictionary mentioned above
def top_3_centuries():
max_nr_works_list = sorted(cachedict.values())
top_3_values = []
for i in range(len(max_nr_works_list)-3, len(max_nr_works_list)):
top_3_values.append(max_nr_works_list[i])
print(top_3_values)
This gives me a list of the max-values I want to lookup. But how do I proceed from here? Is there a way to do this without a reverse-lookup (Which is slow for dictionaries, right?) I have the feeling that I can do this task much more efficiently/pythonic.
You could also use collections.Counter with most_common (which internally uses a heap queue):
from collections import Counter
dct = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49,
9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408,
16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
count = Counter(dct)
print(count.most_common(3)) # [(19, 244675), (20, 115878), (18, 111490)]
heapq.nlargest
You can avoid a full sort here by using a heap queue:
from heapq import nlargest
from operator import itemgetter
dct = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49,
9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408,
16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
res = nlargest(3, dct.items(), key=itemgetter(1))
print(res)
# [(19, 244675), (20, 115878), (18, 111490)]
You can use this:
a = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49,
9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408,
16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
l = sorted(list(a.items()), key=lambda tup: tup[1], reverse=True)[:3]
print(l) # [(19, 244675), (20, 115878), (18, 111490)]
It converts the dictionary a into a list of tuples, sort by tup[1], reverse it and get the first 3 hits.
You can do it like so:
dct = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49, 9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408, 16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
res = [next(k for k in dct if dct[k]==v) for v in sorted(dct.values(), reverse=True)[:3]]
print(res) # -> [19, 20, 18]
Break-down:
sorted(dct.values(), reverse=True)[:3]:: Takes the 3 max dictionary values.
next(k for k in dct if dct[k]==v):: returns the dictionary key, for which the value is one of the above 3 (iteratively).
in two simple steps : aux = sorted([(v,k) for (k,v) in dic.items()]) res = [(v,k) for (k,v) in aux[-3:]] #[(18, 111490), (20, 115878), (19, 244675)] faster than nlargest and Counter.most_common on this example.
This returns what you want:
d = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49, 9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408, 16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
print(sorted([(i,j) for i, j in d.items() if j in (sorted(d.values())[-3:])])[-3:])
#[(18, 111490), (19, 244675), (20, 115878)]
d = {0: 0, 1: 11, 2: 26, 3: 43, 4: 14, 5: 29, 6: 34, 7: 49, 8: 49, 9: 108, 10: 124, 11: 108, 12: 361, 13: 290, 14: 2118, 15: 5408, 16: 43473, 17: 109462, 18: 111490, 19: 244675, 20: 115878, 21: 6960}
d_items_sorted = sorted(d.items(), key=lambda x: x[1], reverse=True)
d_items_sorted[:3]
Returns :
[(19, 244675), (20, 115878), (18, 111490)]
This is the easiest code I could get, but sorting the dictionary cost O(nlogn) and you should be able to do the same in O(n)
Are you looking for the most efficient way or just the optimal way in permormace/algorithm simplicity? If it's the latter may be you should consider sorting dictionary items as tuples (you can get them with cachedict.items()) like in this answer https://stackoverflow.com/a/613218/10453363 Just sort tuples by the value and then get the last 3 tuples (which are key/value pairs)
Is it possible to combine two ranges to create a dictionary?
Is it possible to do something join to dictionaries or two lists to make a dictionary?
a = dict(range(1, 97))
b = dict(range(1, 97))
c = {
a + b
}
Or something like this:
a = range(1, 97)
b = range(1, 97)
c = {
a + b
}
Because I would like to create a dictionary c where the x key is 1 and the y key is 1...until x key is 96 and y key is 96.
This because I would like to crate with tkinter a frame filled with 96 selectable and indipendent elements (as SUNKEN radiobuttons).
I would like to have in my frame something like radiobutton1,1, radiobutton 2,2, radiobutton 3,3 and so on until radiobutton96,96
I will use then
v = tk.IntVar()
v.set("L")
for ax, bx in c:
d = ttk.Radiobutton(master, text=ax,
variable=v, value=bx)
d.pack(anchor=NSWO)
If you have this:
a = range(1, 97)
b = range(1, 97)
and you want to make a dictionary, all you have to do is this:
c = dict(zip(a, b))
It works normally:
>>> c[1]
1
>>> c[14]
14
>>> c
{1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96}