2
我可以使用一些項目的更多幫助。我正在分析450萬行數據。我已經將數據讀入一個數據框，已經組織了數據，現在有3列：1）日期爲日期時間2）唯一標識符3）價格熊貓從一個非統一的日期列表中找到一年前的日期
我需要計算每年物品價格的年度變化，日期不統一，每個項目也不一致。例如：
date item price
12/31/15 A 110
12/31/15 B 120
12/31/14 A 100
6/24/13 B 100
我想是要找到一個結果是：
date item price previousdate % change
12/31/15 A 110 12/31/14 10%
12/31/15 B 120 6/24/13 20%
12/31/14 A 100
6/24/13 B 100
編輯 - 數據
date item price
6/1/2016 A 276.3457646
6/1/2016 B 5.044165645
4/27/2016 B 4.91300186
4/27/2016 A 276.4329163
4/20/2016 A 276.9991265
4/20/2016 B 4.801263717
4/13/2016 A 276.1950213
4/13/2016 B 5.582923328
4/6/2016 B 5.017863509
4/6/2016 A 276.218649
3/30/2016 B 4.64274783
3/30/2016 A 276.554653
3/23/2016 B 5.576438253
3/23/2016 A 276.3135836
3/16/2016 B 5.394435443
3/16/2016 A 276.4222986
3/9/2016 A 276.8929462
3/9/2016 B 4.999951262
3/2/2016 B 4.731349423
3/2/2016 A 276.3972068
1/27/2016 A 276.8458971
1/27/2016 B 4.993033132
1/20/2016 B 5.250379701
1/20/2016 A 276.2899864
1/13/2016 B 5.146639666
1/13/2016 A 276.7041978
1/6/2016 B 5.328296958
1/6/2016 A 276.9465891
12/30/2015 B 5.312301356
12/30/2015 A 256.259668
12/23/2015 B 5.279105491
12/23/2015 A 255.8411198
12/16/2015 B 5.150798234
12/16/2015 A 255.8360529
12/9/2015 A 255.4915183
12/9/2015 B 4.722876886
12/2/2015 A 256.267146
12/2/2015 B 5.083626167
10/28/2015 B 4.876177757
10/28/2015 A 255.6464653
10/21/2015 B 4.551439655
10/21/2015 A 256.1735769
10/14/2015 A 255.9752668
10/14/2015 B 4.693967392
10/7/2015 B 4.911797443
10/7/2015 A 256.2556707
9/30/2015 B 4.262994526
9/30/2015 A 255.8068691
7/1/2015 A 255.7312385
4/22/2015 A 234.6210132
4/15/2015 A 235.3902076
4/15/2015 B 4.154926102
4/1/2015 A 234.4713827
2/25/2015 A 235.1391496
2/18/2015 A 235.1223471
我所做的最好的例子（帶來自其他用戶的一些幫助）沒有工作，但是在下面。感謝您提供的任何幫助或指引我朝着正確的方向發展！
import pandas as pd
import datetime as dt
import numpy as np
df = pd.read_csv('...python test file5.csv',parse_dates =['As of Date'])
df = df[['item','price','As of Date']]
def get_prev_year_price(x, df):
try:
return df.loc[x['prev_year_date'], 'price']
#return np.abs(df.time - x)
except Exception as e:
return x['price']
#Function to determine the closest date from given date and list of all dates
def nearest(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
df['As of Date'] = pd.to_datetime(df['As of Date'],format='%m/%d/%Y')
df = df.rename(columns = {df.columns[2]:'date'})
# list of dates
dtlst = [item for item in df['date']]
data = []
data2 = []
for item in df['item'].unique():
item_df = df[df['item'] == item] #select based on items
select_dates = item_df['date'].unique()
item_df.set_index('date', inplace=True) #set date as key index
item_df = item_df.resample('D').mean().reset_index() #fill in missing date
item_df['price'] = item_df['price'].interpolate('nearest') #fill in price with nearest price available
# use max(item_df['date'] where item_df['date'] < item_df['date'] - pd.DateOffset(years=1, days=1))
#possible_date = item_df['date'] - pd.DateOffset(years=1)
#item_df['prev_year_date'] = max(df[df['date'] <= possible_date])
item_df['prev_year_date'] = item_df['date'] - pd.DateOffset(years=1) #calculate 1 year ago date
date_df = item_df[item_df.date.isin(select_dates)] #select dates with useful data
item_df.set_index('date', inplace=True)
date_df['prev_year_price'] = date_df.apply(lambda x: get_prev_year_price(x, item_df),axis=1)
#date_df['prev_year_price'] = date_df.apply(lambda x: nearest(dtlst, x),axis=1)
date_df['change'] = date_df['price']/date_df['prev_year_price']-1
date_df['item'] = item
data.append(date_df)
data2.append(item_df)
summary = pd.concat(data).sort_values('date', ascending=False)
#print (summary)
#saving the output of the CSV file to see how data looks after being handled
filename = '...python_test_file_save4.csv'
summary.to_csv(filename, index=True, encoding='utf-8')
有沒有在每個項目每年最多一個價格？ –
不幸的是，每年至多會有大約50件產品 – CodinglyClueless
您需要精確定義年份的年份意味着什麼 –