Wie man korrekt formatierten Pandas Dataframe von apply zurückgibt?

Question

Sagen wir folgende Datenrahmen haben:

import pandas as pd 
import numpy as np 

years = [2005, 2006] 
location = ['city', 'suburb'] 
dft = pd.DataFrame({ 
    'year': [years[np.random.randint(0, 1+1)] for _ in range(100)], 
    'location': [location[np.random.randint(0, 1+1)] for _ in range(100)], 
    'days_to_complete': np.random.randint(100, high=600, size=100), 
    'cost_in_millions': np.random.randint(1, high=10, size=100) 
}) 

GROUPBY Jahr und Standort und dann eine Funktion wie die folgenden Bedingungen zutreffen:

def get_custom_summary(group): 
    gt_200 = group.days_to_complete > 200 
    lt_200 = group.days_to_complete < 200 

    avg_days_gt200 = group[gt_200].days_to_complete.mean() 
    avg_cost_gt200 = group[gt_200].cost_in_millions.mean() 

    avg_days_lt200 = group[lt_200].days_to_complete.mean() 
    avg_cost_lt200 = group[lt_200].cost_in_millions.mean() 

    lt_200_prop = lt_200.sum()/(gt_200.sum() + lt_200.sum()) 

    return pd.DataFrame({ 
     'gt_200': {'AVG_DAYS': avg_days_gt200, 'AVG_COST': avg_cost_gt200}, 
     'lt_200': {'avg_days': avg_days_lt200, 'avg_cost': avg_cost_lt200}, 
     'lt_200_prop' : lt_200_prop 
    }) 

result = dft.groupby(['year', 'location']).apply(get_custom_summary) 

Aufruf Entstapelungsunterdrückung (2) auf das Ergebnis erhalten wir die folgende Ausgabe:

print(result.unstack(2)) 

       gt_200         lt_200        lt_200_prop        
       AVG_COST AVG_DAYS avg_cost avg_days AVG_COST AVG_DAYS avg_cost avg_days AVG_COST AVG_DAYS avg_cost avg_days 
year location                              
2005 city  4.818182 415.636364  NaN  NaN  NaN  NaN 7.250000 165.50 0.153846 0.153846 0.153846 0.153846 
    suburb 5.631579 336.631579  NaN  NaN  NaN  NaN 5.166667 140.50 0.240000 0.240000 0.240000 0.240000 
2006 city  4.130435 396.913043  NaN  NaN  NaN  NaN 5.750000 150.75 0.258065 0.258065 0.258065 0.258065 
    suburb 5.294118 392.823529  NaN  NaN  NaN  NaN 1.000000 128.00 0.055556 0.055556 0.055556 0.055556 

Für die Spalten gt_200 und lt_200 ein Aufruf an dropna(axis=1) wird remov e die Spalten gefüllt mit NaN, aber die lt_200_prop Spalte ist immer noch mit den falschen Spaltennamen stecken. Wie kann ich einen Datenrahmen von get_custom_summary zurück, die nicht ausgestrahlt wird (wenn das das richtige Wort ist), um die Unterspalten (AVG_COST, AVG_DAYS, avg_cost, avg_days) an die Säulen (gt_200, lt_200, lt_200_prop)?

EDIT:

gewünschte Ausgabe:

    gt_200    lt_200   lt_200_prop        
       AVG_COST AVG_DAYS avg_cost avg_days 
year location                              
2005 city  4.818182 415.636364 7.250000 165.50 0.153846 
    suburb 5.631579 336.631579 5.166667 140.50 0.240000 
2006 city  4.130435 396.913043 5.750000 150.75 0.258065 
    suburb 5.294118 392.823529 1.000000 128.00 0.055556 

Answer 1

Return ein Datenrahmen mit Spalten gleich einem MultiIndex gesetzt.

from collections import OrderedDict 

def get_multi_index(ordered_dict): 
    length = len(list(ordered_dict.values())[0]) 

    for k in ordered_dict: 
     assert(len(ordered_dict[k]) == length) 

    names = list() 
    arrays = list() 
    for k in ordered_dict: 
     names.append(k) 
     arrays.append(np.array(ordered_dict[k])) 

    tuples = list(zip(*arrays)) 
    return pd.MultiIndex.from_tuples(tuples, names=names) 

def get_custom_summary(group): 
    gt_200 = group.days_to_complete > 200 
    lt_200 = group.days_to_complete < 200 

    avg_days_gt_200 = group[gt_200].days_to_complete.mean() 
    avg_cost_gt_200 = group[gt_200].cost_in_millions.mean() 

    avg_days_lt_200 = group[lt_200].days_to_complete.mean() 
    avg_cost_lt_200 = group[lt_200].cost_in_millions.mean() 

    lt_200_prop = lt_200.sum()/(gt_200.sum() + lt_200.sum()) 

    ordered_dict = OrderedDict() 
    ordered_dict['first'] = ['lt_200', 'lt_200', 'gt_200', 'gt_200', 'lt_200_prop'] 
    ordered_dict['second'] = ['avg_cost', 'avg_days', 'AVG_COST', 'AVG_DAYS', 'prop'] 

    data = [[avg_cost_lt_200, avg_days_lt_200, avg_cost_gt_200, avg_days_gt_200, lt_200_prop]] 
    return pd.DataFrame(data, columns=get_multi_index(ordered_dict)) 

Get und Drucker:

result = dft.groupby(['year', 'location']).apply(get_custom_summary).xs(0, level=2) 
print(result) 

Ausgang:

first   lt_200    gt_200    lt_200_prop 
second   avg_cost avg_days AVG_COST AVG_DAYS  prop 
year location               
2005 city  7.555556 135.444444 5.300000 363.750000 0.310345 
    suburb 5.000000 137.333333 5.555556 444.222222 0.250000 
2006 city  6.250000 169.000000 4.714286 422.380952 0.160000 
    suburb 4.428571 133.142857 4.333333 445.666667 0.318182 

Answer 2

Meine Lösung ist die gleichen Spaltennamen in Funktion get_custom_summary in gt_200 und lt_200 verwenden und es dann durch die Funktion umbenennen str.lower und letzten col benutzerdefinierte Spaltennamen hinzufügen.

Aber es gibt MultiIndex, so müssen Sie schaffen neue von MultiIndex.from_tuples:

years = [2005, 2006] 
location = ['city', 'suburb'] 
np.random.seed(1234) 
dft = pd.DataFrame({ 
    'year': [years[np.random.randint(0, 1+1)] for _ in range(100)], 
    'location': [location[np.random.randint(0, 1+1)] for _ in range(100)], 
    'days_to_complete': np.random.randint(100, high=600, size=100), 
    'cost_in_millions': np.random.randint(1, high=10, size=100) 
}) 

def get_custom_summary(group): 
    gt_200 = group.days_to_complete > 200 
    lt_200 = group.days_to_complete < 200 

    avg_days_gt200 = group[gt_200].days_to_complete.mean() 
    avg_cost_gt200 = group[gt_200].cost_in_millions.mean() 

    avg_days_lt200 = group[lt_200].days_to_complete.mean() 
    avg_cost_lt200 = group[lt_200].cost_in_millions.mean() 

    lt_200_prop = (lt_200).sum()/((gt_200).sum() + (lt_200).sum()) 

    return pd.DataFrame({ 
     'gt_200': {'AVG_DAYS': avg_days_gt200, 'AVG_COST': avg_cost_gt200}, 
     'lt_200': {'AVG_DAYS': avg_days_lt200, 'AVG_COST': avg_cost_lt200}, 
     'lt_200_prop' : lt_200_prop 
    }) 

result = dft.groupby(['year', 'location']).apply(get_custom_summary).unstack(2) 
#drop last column with duplicates values 
result = result.drop(result.columns[[-1]], axis=1) 

#rename columns names in level 1 
a = (result.columns.get_level_values(1)) 
level1 = a[:2].union(a[2:4].str.lower().union(['col'])) 
cols = list(zip(result.columns.get_level_values(0),level1)) 
result.columns = pd.MultiIndex.from_tuples(cols) 

print (result) 
       gt_200    lt_200    lt_200_prop 
       AVG_COST AVG_DAYS avg_cost avg_days   col 
year location               
2005 city  5.238095 392.095238 5.500000 144.666667 0.222222 
    suburb 4.428571 427.095238 4.000000 167.666667 0.125000 
2006 city  4.368421 406.789474 4.571429 150.142857 0.269231 
    suburb 4.000000 439.062500 4.142857 145.142857 0.304348 

---

Einfachere Lösung ist Spalten entfernen:

result = dft.groupby(['year', 'location']).apply(get_custom_summary).unstack(2) 
#drop last 3 column, then drop NaN columns 
result = result.drop(result.columns[[-1, -2, -3]], axis=1).dropna(axis=1) 
print (result) 
       gt_200    lt_200    lt_200_prop 
       AVG_COST AVG_DAYS avg_cost avg_days AVG_COST 
year location               
2005 city  5.238095 392.095238 5.500000 144.666667 0.222222 
    suburb 4.428571 427.095238 4.000000 167.666667 0.125000 
2006 city  4.368421 406.789474 4.571429 150.142857 0.269231 
    suburb 4.000000 439.062500 4.142857 145.142857 0.304348