notebook.community

Edit and run 





In [140]:



    


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
import calendar



    











In [67]:



    


test_date = dt.datetime(2017,8,25)



    











In [237]:



    


df = pd.read_csv("transactions.csv")
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index(df['Date']).drop('Date',axis=1)



    











In [254]:



    


x = pd.to_datetime('2017-08-25')



    











In [239]:



    


def process_raw(tab):
    df = tab
    df['Date'] = pd.to_datetime(df['Date'])
    df = df.set_index(df['Date']).drop('Date',axis=1)
    return df



    











In [240]:



    


process_raw(pd.read_csv("transactions.csv"))



    


















    
Out[240]:











  
    

      
      Description
      Original Description
      Amount
      Transaction Type
      Category
      Account Name
      Labels
      Notes
    

    

      Date
      
      
      
      
      
      
      
      
    

  
  
    

      2017-09-09
      Gig Car Share
      GIG CAR SHARE
      0.50
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-09-09
      Gig Car Share
      GIG CAR SHARE
      0.50
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-09-10
      Lyft
      LYFT *RIDE SUN 2PM 855-280-0278 CA
      15.38
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-09-10
      Lyft
      LYFT *RIDE SUN 4PM 855-280-0278 CA
      10.88
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-09-09
      Cafe Durant
      CAFE DURANT BERKELEY CA
      9.66
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-09-08
      Diyar Mediterranean Cusi
      DIYAR MEDITERRANEAN CUSI BERKELEY CA
      8.73
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-31
      Raleighs Pub
      RALEIGHS PUB BERKELEY CA
      10.87
      debit
      Alcohol & Bars
      Discover
      NaN
      NaN
    

    

      2017-08-31
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      225.61
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-08-29
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      155.53
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-08-29
      Ucb Recreational Sports
      UCB RECREATIONAL SPORTS 510-642-7796 CA
      25.00
      debit
      Gym
      Discover
      NaN
      NaN
    

    

      2017-08-27
      Berkeley Bowl Markesbe
      BERKELEY BOWL MARKESBE BERKELEY CA
      200.61
      debit
      Groceries
      Discover
      NaN
      NaN
    

    

      2017-08-26
      Chengdu Style Restaurant
      CHENGDU STYLE RESTAURANT BERKELEY CA
      63.00
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-25
      V A Cafe
      SQ *V&A CAFE BERKELEY CA0002305843011213890465
      3.00
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 12PM 855-280-0278 CA
      11.69
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 12PM 855-280-0278 CA
      35.11
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-25
      L Epi D
      SQ *L'EPI D'OR BAKERY CUPERTINO CA000115292150...
      4.99
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 10AM 855-280-0278 CA
      30.85
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-24
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      383.82
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-08-23
      Laval's Pizza
      LAVAL'S PIZZA BERKELEY CA
      6.89
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-08-21
      Chick-Fil-A
      CHICK-FIL-A #02765 WALNUT CREEK CA
      8.39
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-08-21
      Tea 1
      SQ *TEA 1 BERKELEY BERKELEY CA0002305843011209...
      4.25
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-08-21
      Amazon
      AMAZON.COM AMZN.COM/BILLWA6M1Q94EWTEI
      15.32
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2017-08-20
      Amazon
      AMAZON.COM AMZN.COM/BILLWA49VLZN1FMGG
      15.42
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2017-08-20
      Lyft
      LYFT *RIDE SUN 7PM 855-280-0278 CA
      6.50
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-19
      Comcast
      COMCAST CALIFORN CS 1X 800-266-2278 CA73603189...
      39.99
      debit
      Television
      Discover
      NaN
      NaN
    

    

      2017-08-18
      Lyft
      LYFT *RIDE FRI 8PM 855-280-0278 CA
      7.09
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-17
      Laval's Pizza
      LAVAL'S PIZZA BERKELEY CA
      8.75
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-08-13
      Diyar Mediterranean Cusi
      DIYAR MEDITERRANEAN CUSI BERKELEY CA
      18.55
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-12
      Lyft
      LYFT *RIDE SAT 3PM 855-280-0278 CA
      6.50
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-12
      Safeway
      SAFEWAY STORE 0691 BERKELEY CA
      93.20
      debit
      Groceries
      Discover
      NaN
      NaN
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2016-11-10
      Ucafe Berkeley
      SQ *UCAFE, BERKELEY BERKELEY CA000115292150743...
      2.75
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-11-10
      La Val's Pizza
      LAVAL'S PIZZA BERKELEY CA
      9.30
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2016-11-09
      Dumpling Express
      DUMPLING EXPRESS BERKELEY CA
      8.54
      debit
      Food & Dining
      Discover
      NaN
      NaN
    

    

      2016-11-06
      Ation 723 Tea Street
      723 TEA STATION BERKELEY CA
      8.27
      debit
      Coffee Shops
      Discover
      NaN
      NaN
    

    

      2016-11-04
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WAVHOYB211YRP
      25.61
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-11-03
      Artichokes
      SQ *ARTICHOKE BASILLE' BERKELEY CA000115292150...
      5.20
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-11-02
      Cafe Strada Berkeley
      CAFE STRADA, INC BERKELEY CA
      2.95
      debit
      Coffee Shops
      Discover
      NaN
      NaN
    

    

      2016-11-02
      Amazon
      AMAZON.COM AMZN.COM/BILLWAS92AN8Q6MPU
      12.41
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-30
      Dumpling Express
      DUMPLING EXPRESS BERKELEY CA
      8.54
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-10-30
      Amazon
      AMAZON MKTPLACE PMTS AMZN.COM/BILLWAQXI26QETC0G
      10.94
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-30
      Dumpling Express
      DUMPLING EXPRESS BERKELEY CA
      8.54
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-10-29
      Amazon
      AMAZON MKTPLACE PMTS AMZN.COM/BILLWAQXIRBS5R0F1
      25.97
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-26
      Sharetea
      SQ *SHARETEA BERKELEY CA0001152921507419964410
      4.10
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2016-10-24
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WAEIYIHN10TWM
      17.37
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-23
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      136.10
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2016-10-23
      Ucb Housing Dining
      UCB HOUSING&DINING SVCS 510-643-6955 CA
      20.00
      debit
      Transfer for Cash Spending
      Discover
      NaN
      NaN
    

    

      2016-10-16
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WAG4Q9R3IKRN9
      14.22
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-15
      Dumpling Express
      DUMPLING EXPRESS BERKELEY CA
      8.04
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-10-15
      Amazon
      AMAZON MKTPLACE PMTS AMZN.COM/BILLWAKYG48B5SZM0
      16.95
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-15
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WAI8CZU4V6UT0
      11.60
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-10-14
      Cafe Strada Berkeley
      CAFE STRADA, INC BERKELEY CA
      1.95
      debit
      Coffee Shops
      Discover
      NaN
      NaN
    

    

      2016-10-07
      Dumpling Express
      DUMPLING EXPRESS BERKELEY CA
      8.54
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-10-01
      Sweetheart Cafe
      SQ *SWEETHEART CAFE BERKELEY CA000115292150739...
      3.11
      debit
      Coffee Shops
      Discover
      NaN
      NaN
    

    

      2016-09-30
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WAQNFQ0I2N5VZ
      12.46
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-09-26
      Amazon
      AMAZON MKTPLACE PMTS AMZN.COM/BILLWAPSZW18FL65W
      12.95
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-09-26
      Simply Bowl
      SIMPLY BOWL BERKELEY CA
      11.50
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2016-09-25
      Amazon
      AMAZON MKTPLACE PMTS AMZN.COM/BILLWAILEP1GHK5UI
      4.95
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-09-25
      Ucb Housing Dining
      UCB HOUSING&DINING SVCS 510-643-6955 CA
      20.00
      debit
      Transfer for Cash Spending
      Discover
      NaN
      NaN
    

    

      2016-09-24
      Amazon
      AMAZON PICKUP POINTS 877-345-0975 WATZP495DA69Q
      9.83
      debit
      Shopping
      Discover
      NaN
      NaN
    

    

      2016-09-24
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      234.66
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

  



288 rows × 8 columns



















In [51]:



    


to = dt.datetime.today()



    











In [139]:



    


categories = set(df['Category'])
print(categories)



    


















    






{'Pharmacy', 'Public Transportation', 'Tuition', 'Gift', 'Advertising', 'Sporting Goods', 'Restaurants', 'Clothing', 'Transfer for Cash Spending', 'Rental Car & Taxi', 'Television', 'Groceries', 'Credit Card Payment', 'Air Travel', 'Doctor', 'Gym', 'Fast Food', 'Alcohol & Bars', 'Food & Dining', 'Shopping', 'Mortgage & Rent', 'Hair', 'Coffee Shops'}

















In [273]:



    


getRangeExpenditure(df, to,to)



    


















    
Out[273]:








0

















In [258]:



    


def getTodayExpenditure(tab, today=dt.datetime.today()):
    '''gets amount of money spent toady'''
    if (today not in tab.index):
        return 0
    today_transactions = tab.loc[today.date()]
    return sum(today_transactions['Amount'])



    











In [79]:



    


test = getTodayExpenditure(df,test_date)
print("$",round(test,2))



    


















    






$ 85.64

















In [312]:



    


to



    


















    
Out[312]:








datetime.datetime(2017, 9, 15, 23, 30, 27, 195687)

















In [313]:



    


to - dt.timedelta(days=7)



    


















    
Out[313]:








datetime.datetime(2017, 9, 8, 23, 30, 27, 195687)

















In [263]:



    


a = [i for i in pd.date_range(to, to).values if i in df.index.values]



    











In [332]:



    


getFrequencies(df, test_date,test_date)



    


















    
Out[332]:








{'Advertising': 0,
 'Air Travel': 0,
 'Alcohol & Bars': 0,
 'Clothing': 0,
 'Coffee Shops': 0,
 'Credit Card Payment': 0,
 'Doctor': 0,
 'Fast Food': 0,
 'Food & Dining': 0,
 'Gift': 0,
 'Groceries': 0,
 'Gym': 0,
 'Hair': 0,
 'Mortgage & Rent': 0,
 'Pharmacy': 0,
 'Public Transportation': 0,
 'Rental Car & Taxi': 77.650000000000006,
 'Restaurants': 7.9900000000000002,
 'Shopping': 0,
 'Sporting Goods': 0,
 'Television': 0,
 'Transfer for Cash Spending': 0,
 'Tuition': 0}

















In [324]:



    


date_range = [i for i in pd.date_range(test_date, test_date).values if i in df.index.values]



    











In [326]:



    


transaction_range



    


















    
Out[326]:











  
    

      
      Description
      Original Description
      Amount
      Transaction Type
      Category
      Account Name
      Labels
      Notes
    

    

      Date
      
      
      
      
      
      
      
      
    

  
  
    

      2017-08-25
      V A Cafe
      SQ *V&A CAFE BERKELEY CA0002305843011213890465
      3.00
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 12PM 855-280-0278 CA
      11.69
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 12PM 855-280-0278 CA
      35.11
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

    

      2017-08-25
      L Epi D
      SQ *L'EPI D'OR BAKERY CUPERTINO CA000115292150...
      4.99
      debit
      Restaurants
      Discover
      NaN
      NaN
    

    

      2017-08-25
      Lyft
      LYFT *RIDE FRI 10AM 855-280-0278 CA
      30.85
      debit
      Rental Car & Taxi
      Discover
      NaN
      NaN
    

  




















In [268]:



    


def getRangeExpenditure(tab, date_start, date_end, category=None):
    '''returns the amount of money spent in date range'''
    date_range = [i for i in pd.date_range(date_start, date_end).values if i in tab.index.values]
    if (len(date_range)==0):
        return 0
    today_transactions = tab.loc[date_range]
    today_transactions.head()
    if (category==None):
        return sum(today_transactions['Amount'])
    else:
        return sum(today_transactions[today_transactions['Category']==category]['Amount'])



    











In [269]:



    


getRangeExpenditure(df, (dt.datetime.today()-dt.timedelta(days=60)).date(),dt.datetime.today().date(), 'Restaurants')



    


















    
Out[269]:








129.41000000000003

















In [330]:



    


def getFrequencies(tab, start_day, end_day, freq='daily'):
    date_range = [i for i in pd.date_range(start_day, end_day).values if i in tab.index.values]
    out = create_categories_map(tab)
    if (len(date_range)==0):
        return out
    transaction_range = tab.loc[date_range]
    out_val = transaction_range.groupby('Category')['Amount'].sum().to_dict()
    diff_days = (end_day - start_day).days
    if (diff_days==0):
        diff_days = 1
        
    if (freq == 'weekly'):
        r = diff_days/7
    elif (freq=='monthly'):
        r = diff_days/30
    elif (freq=='yearly'):
        r = diff_days/365
    else:
        r = diff_days
    for k in out_val:
        out_val[k] = out_val[k]/r
        out[k] = out_val[k]
    return out



    











In [331]:



    


def getFrequenciesCounts(tab, start_day, end_day, freq='daily'):
    date_range = [i for i in pd.date_range(start_day, end_day).values if i in tab.index.values]
    out = create_categories_map(tab)
    if (len(date_range)==0):
        return out
    transaction_range = tab.loc[date_range]
    out_val = transaction_range['Category'].value_counts().to_dict()
    diff_days = (end_day - start_day).days
    if (diff_days==0):
        diff_days = 1
        
    if (freq == 'weekly'):
        r = diff_days/7
    elif (freq=='monthly'):
        r = diff_days/30
    elif (freq=='yearly'):
        r = diff_days/365
    else:
        r = diff_days
    for k in out_val:
        out_val[k] = out_val[k]/r
        out[k] = out_val[k]
    return out



    











In [306]:



    


getFrequenciesCounts(df, x, to)



    


















    
Out[306]:








{'Advertising': 0,
 'Air Travel': 0,
 'Alcohol & Bars': 0.047619047619047616,
 'Clothing': 0,
 'Coffee Shops': 0,
 'Credit Card Payment': 0.095238095238095233,
 'Doctor': 0,
 'Fast Food': 0,
 'Food & Dining': 0,
 'Gift': 0,
 'Groceries': 0.047619047619047616,
 'Gym': 0.047619047619047616,
 'Hair': 0,
 'Mortgage & Rent': 0,
 'Pharmacy': 0,
 'Public Transportation': 0,
 'Rental Car & Taxi': 0.33333333333333331,
 'Restaurants': 0.23809523809523808,
 'Shopping': 0,
 'Sporting Goods': 0,
 'Television': 0,
 'Transfer for Cash Spending': 0,
 'Tuition': 0}

















In [201]:



    


def getOccurrencies(tab, start_day, end_day, freq='daily'):
    transaction_range = tab.loc[[i for i in pd.date_range(start_day, end_day).values if i in tab.index.values]]
    out = transaction_range['Category'].value_counts().to_dict()
    return out



    











In [335]:



    


(test_date+dt.timedelta(days=2)).weekday()



    


















    
Out[335]:








6

















In [154]:



    


def getFrequencyDate(time, freq='daily'):
    """returns the start and end dates based on the given frequency
    possible freq = 'daily', 'weekly', 'monthly', 'yearly' """
    if (freq=='weekly'):
        start = time - dt.timedelta(days=time.weekday())
        end = start + dt.timedelta(days=6)
    elif (freq=='monthly'):
        start = time.replace(day=1)
        start_day, end_day = calendar.monthrange(time.year,time.month)
        end = time.replace(day=end_day)
    elif (freq=='yearly'):
        start = time.replace(month=1).replace(day=1)
        end = time.replace(month=12).replace(day=31)
    else:
        start = time
        end = time
    return start, end



    











In [251]:



    


x = pd.to_datetime(to)



    











In [294]:



    


overall_f = full_category_frequencies(df,'weekly')



    











In [296]:



    


overall_f



    


















    
Out[296]:








{'Advertising': 1.4957264957264957,
 'Air Travel': 1.8586894586894587,
 'Alcohol & Bars': 0.21678062678062676,
 'Clothing': 1.0916809116809116,
 'Coffee Shops': 1.2155270655270656,
 'Credit Card Payment': 99.655868945868932,
 'Doctor': 0.39886039886039881,
 'Fast Food': 5.5441595441595464,
 'Food & Dining': 1.5386039886039886,
 'Gift': 0.56757834757834758,
 'Groceries': 14.787948717948717,
 'Gym': 1.396011396011396,
 'Hair': 0.39886039886039881,
 'Mortgage & Rent': 0.99715099715099709,
 'Pharmacy': 0.63159544159544156,
 'Public Transportation': 3.2457264957264957,
 'Rental Car & Taxi': 5.3281766381766396,
 'Restaurants': 16.805384615384611,
 'Shopping': 33.906125356125344,
 'Sporting Goods': 1.9434472934472933,
 'Television': 3.4890313390313388,
 'Transfer for Cash Spending': 3.1908831908831905,
 'Tuition': 1.396011396011396}

















In [293]:



    


def full_category_frequencies(tab, freq='Daily'):
    return getFrequencies(tab, min(tab.index), max(tab.index), freq)



    











In [297]:



    


def full_category_frequencies_counts(tab, freq='Daily'):
    return getFrequenciesCounts(tab, min(tab.index), max(tab.index), freq)



    











In [236]:



    


df[df['Description'] == 'Tea Cory Hall']



    


















    
Out[236]:











  
    

      
      Description
      Original Description
      Amount
      Transaction Type
      Category
      Account Name
      Labels
      Notes
    

    

      Date
      
      
      
      
      
      
      
      
    

  
  
    

      2017-04-25
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0001152921507...
      4.25
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-04-14
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0002305843011...
      4.00
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-04-03
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0001152921507...
      4.25
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-02-17
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0002305843010...
      4.25
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2017-01-27
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0002305843010...
      4.25
      debit
      Fast Food
      Discover
      NaN
      NaN
    

    

      2016-11-29
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0001152921507...
      4.00
      debit
      Coffee Shops
      Discover
      NaN
      NaN
    

    

      2016-11-21
      Tea Cory Hall
      SQ *TEA 1 - CORY HALL BERKELEY CA0001152921507...
      4.00
      debit
      Fast Food
      Discover
      NaN
      NaN
    

  




















In [249]:



    


df.index



    


















    
Out[249]:








DatetimeIndex(['2017-09-09', '2017-09-09', '2017-09-10', '2017-09-10',
               '2017-09-09', '2017-09-08', '2017-08-31', '2017-08-31',
               '2017-08-29', '2017-08-29',
               ...
               '2016-10-14', '2016-10-07', '2016-10-01', '2016-09-30',
               '2016-09-26', '2016-09-26', '2016-09-25', '2016-09-25',
               '2016-09-24', '2016-09-24'],
              dtype='datetime64[ns]', name='Date', length=288, freq=None)

















In [277]:



    


def create_categories_map(tab, label='Category'):
    cat_set = set(tab[label])
    out = dict()
    for c in cat_set:
        out[c] = 0
    return out



    











In [308]:



    


getFrequencies(df, pd.to_datetime("2017-08-01"),pd.to_datetime("2017-09-01"),'weekly')



    


















    
Out[308]:








{'Advertising': 0,
 'Air Travel': 0,
 'Alcohol & Bars': 2.4545161290322577,
 'Clothing': 0,
 'Coffee Shops': 0,
 'Credit Card Payment': 172.73290322580644,
 'Doctor': 0,
 'Fast Food': 6.3858064516129032,
 'Food & Dining': 2.0254838709677419,
 'Gift': 0,
 'Groceries': 68.191290322580642,
 'Gym': 5.6451612903225801,
 'Hair': 0,
 'Mortgage & Rent': 0,
 'Pharmacy': 0,
 'Public Transportation': 0,
 'Rental Car & Taxi': 23.260322580645159,
 'Restaurants': 20.218709677419351,
 'Shopping': 25.213548387096772,
 'Sporting Goods': 0,
 'Television': 9.0299999999999994,
 'Transfer for Cash Spending': 0,
 'Tuition': 0}

















In [307]:



    


full_category_frequencies(df, 'weekly')



    


















    
Out[307]:








{'Advertising': 1.4957264957264957,
 'Air Travel': 1.8586894586894587,
 'Alcohol & Bars': 0.21678062678062676,
 'Clothing': 1.0916809116809116,
 'Coffee Shops': 1.2155270655270654,
 'Credit Card Payment': 99.655868945868917,
 'Doctor': 0.39886039886039881,
 'Fast Food': 5.5441595441595437,
 'Food & Dining': 1.5386039886039884,
 'Gift': 0.56757834757834758,
 'Groceries': 14.787948717948717,
 'Gym': 1.396011396011396,
 'Hair': 0.39886039886039881,
 'Mortgage & Rent': 0.99715099715099709,
 'Pharmacy': 0.63159544159544156,
 'Public Transportation': 3.2457264957264957,
 'Rental Car & Taxi': 5.3281766381766396,
 'Restaurants': 16.805384615384611,
 'Shopping': 33.906125356125365,
 'Sporting Goods': 1.9434472934472933,
 'Television': 3.4890313390313392,
 'Transfer for Cash Spending': 3.1908831908831905,
 'Tuition': 1.396011396011396}

















In [299]:



    


df[df['Category']=='Credit Card Payment']



    


















    
Out[299]:











  
    

      
      Description
      Original Description
      Amount
      Transaction Type
      Category
      Account Name
      Labels
      Notes
    

    

      Date
      
      
      
      
      
      
      
      
    

  
  
    

      2017-08-31
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      225.61
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-08-29
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      155.53
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-08-24
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      383.82
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-07-26
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      236.18
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-07-17
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      375.09
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-07-06
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      300.00
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-06-19
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      200.00
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-06-15
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      149.70
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-06-10
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      390.83
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-06-01
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      351.22
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-05-18
      Transfer from BofA Core Checking
      INTERNET PAYMENT - THANK YOU
      406.59
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-04-24
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      389.28
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-03-21
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      159.88
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2017-02-26
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      374.19
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2016-12-23
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      254.90
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2016-11-25
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      273.45
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2016-10-23
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      136.10
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

    

      2016-09-24
      Internet Thank You
      INTERNET PAYMENT - THANK YOU
      234.66
      credit
      Credit Card Payment
      Discover
      NaN
      NaN
    

  




















In [309]:



    


data = pd.read_excel('age.xls')



    











In [311]:



    


data.head()



    


















    
Out[311]:











  
    

      
      Table 3. Age of reference person: Average annual expenditures and characteristics, Consumer Expenditure Survey, 2011
      Unnamed: 1
      Unnamed: 2
      Unnamed: 3
      Unnamed: 4
      Unnamed: 5
      Unnamed: 6
      Unnamed: 7
      Unnamed: 8
      Unnamed: 9
    

  
  
    

      0
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      1
      Item
      All consumer units
      Under 25 years
      25-34 years
      35-44 years
      45-54 years
      55-64 years
      65 years and older
      65-74 years
      75 years and older
    

    

      2
      Number of consumer units (in thousands)
      122287
      7743
      20463
      21699
      24821
      21688
      25873
      14079
      11794
    

    

      3
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      4
      Consumer unit characteristics:                ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

  




















In [ ]:

Content source: jettjz/XpendiBuddy

Similar notebooks:

notebook.community | gallery | about