谁应该在预算上过得好？

介绍

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几个月前，在一次IT活动上，我碰巧看到了Pandas的作品。和他一起工作的那个人没有什么特别令人惊讶的。但是对值的简单加法，平均值的计算，分组是如此精巧地进行，以至于即使我对Python充满偏见，我也着迷了。在2004年至2019年这段时间里，根据资本修复对相当不错的数据集进行了操作。数以十万计的行，但是一切工作都非常快。

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01 , - . . - . - . Pandas, . levenshtein_merge(). join Pandas DataFrame , . , . , , , , pip install Pandas DataFrame. , prepack.

2 , jupyter notebook .

#    
import pandas as pd
import numpy as np
import os, sys

#    
from prepack import prepack as pp

#      
pd.options.display.max_rows = 2000
pd.options.display.max_columns = 200
pd.options.display.max_colwidth = 500
pd.options.display.min_rows = 40

prepack. zip . file-like ,
. pd.read_excel(), excel.

names, files = pp.read_zip("raw_data.zip") 

pd.read_excel() , excel .

read_excel excel , .

, . . , .

pp.read_excel(files[0]).iloc[8:13,:]

, . . excel, 1 DataFrame, , .

3 : files, columns, fltr. excel , — , . — , , . 4 . .

, , 0 11 , , , . , . 3 :
8,9,10

#      1  DataFrame
df = pp.parse_excels(files, columns=[0,12], fltr={0: 'istext',1: 'isnum'}, header=[8,9,10])

#,  
df.head(5)

1 , pickle, prepack 0.4.2 pkl.gz

#   
df.to_csv('raw_data.csv.gz')
pp.save(df, 'raw_data.pkl.gz')

prepack. DataFrame, .

DataFrame, , , . , 3 iloc=True.

, , " " " ", . .

#         
f = pp.df_filter_and(df, {0: 'istext', 1: 'isnum', 2: 'isblank', 3: 'isblank'}, iloc=True)
ppp = df[f]
idx = ppp[' '].index
ppp = ppp.loc[idx,:]

, DataFrame , .

#        ,   
def df_filter(df, fltr):
    f = pp.df_filter_and(df, fltr)
    res = df[f]
    df.drop(res.index, axis=0, inplace=True)
    return res

, DataFrame .

, , . , . label . , ,
, .

def df_filter_post_proc(df, fltr, rzpr, label):
    df_ = df_filter(df, fltr)
    codes = df_['    '].unique()
    names = df_[' '].unique()
    return {'name': list(names), 'ppp': list(codes), 'rzpr': list(rzpr), 'label': list(label)}

, . , , 1 . . df . , , , . . . , : 1 , . . , .

.

#              
def groups_fill(df, groups):
    res = pd.DataFrame([])
    for g in groups:
        el = groups[g]

        ppp = el['ppp']
        rzpr = el['rzpr']
        label = el['label']

        #  ppp ,    -  
        if len(ppp) == 0:
            continue

        # 1   
        f1 = {'    ': ppp}
        # 2    ,      
        f2 = {'  ': rzpr}
        # 3 ,       
        f3 = {'  ': 'isblank'}
        # 3 ,       
        f4 = {'  ': 'isblank'}

        #  
        m1 = pp.df_filter_or(df, f1)
        m2 = pp.df_filter_or(df, f2)
        m3 = pp.df_filter_or(df, f3)
        m4 = pp.df_filter_or(df, f4)
        #       
        df1 = df[m1 & m2 & m3 & m4]

        #     ppp,    label ( )
        #  ppp      
        df_ = df[m1 & m4]
        f5 = {' ': label}
        m5 = pp.df_filter_or(df_, f5)
        df2 = df_[m5].copy()

        #          label (df2), / 
        #   /  rzpr (df1).      label   ,
        # / ,     /     ,      .
        #       3 : , ,  (src_filename)

        #   df1  df2
        #     df2,   
        df2['idx'] = df2.index
        intersect = pd.merge(df1.loc[:,['    ','  ', 'src_filename']],
                             df2.loc[:,['    ','  ', 'src_filename','idx']],
                             on=['    ','  ', 'src_filename'], how='inner')

        #    df2  
        df2_filtered = df2.drop(intersect['idx'], axis=0)

        # ,    df1  df2   1 ,   
        df3 = pd.concat([df1, df2_filtered], axis=0)
        v = df3[[' ',
                 '    ',
                 '  ',
                 '(. .) ',
                 'src_filename']].copy()
        #  
        v['name'] = g
        #    
        res = pd.concat([res, v], axis=0)
    return res

.

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lst = {}

#        
rzpr_base = ['0101','0102','0103','0104','0105','0106','0107']

#      
label_base = ['      ',
              r'contains=.*(?:.*).*',
              r'contains=.*(?:   ).*',
              r'contains=.*(?:  .*).*'
             ]

#           
f = {' ': r'contains=.*(?: ).*'}
rzpr = ['isblank']
df_slice = df_filter_post_proc(ppp, f, rzpr, label_base)
lst[''] = df_slice

rzpr = ['0101','0102','0103','0104','0105','0106','0107','0201']
f = {' ': r'contains=.*(?:|| |||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

rzpr = ['0101','0102','0103','0104','0105','0106','0107','0201']
f = {' ': r'contains=.*(?:|-| ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*(?:||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['  '] = df_slice

f = {' ': r'contains=.*(?:|).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?: | ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*(?: ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*(?:|||| || ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice 

f = {' ': r'contains=.*(?:|| | |||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['  '] = df_slice 

f = {' ': r'contains=.*(?:).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] =df_slice

f = {' ': r'contains=.*(?:).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] =df_slice

f = {' ': r'contains=.*(?:|| |).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] =df_slice

f = {' ': r'contains=.*(?:|||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[',   '] =df_slice

f = {' ': r'contains=.*(?:| | |||||| | | | | ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[', , , '] = df_slice

f = {' ': r'contains=.*(?:|).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['  '] = df_slice

f = {' ': r'contains=.*(?:||||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['  '] = df_slice

f = {' ': r'contains=.*(?:||).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:||| | |).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:| | || | | ).*'}
label = label_base.copy()
label = label + [' ',' ']
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[',   '] = df_slice

f = {' ': r'contains=.*(?:| | | ).*'}
label = label_base.copy()
label = label + [' ']
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label)
lst[''] = df_slice

f = {' ': r'contains=.*(?:).*'}
label = label_base.copy()
label = label + [r'contains=.* .*']
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label)
lst[' '] = df_slice

f = {' ': r'contains=.*(?:| ).*'}
label = label_base.copy()
label = label + ['- ']
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label)
lst[''] = df_slice

f = {' ': r'contains=.*(?:|).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*(?: |||| |).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[', , , '] = df_slice

f = {' ': r'contains=.*(?:| |).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:||| | ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['  '] = df_slice

f = {' ': r'contains=.*(?:).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?:[]|).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

f = {' ': r'contains=.*(?: |  ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst['    '] = df_slice

f = {' ': r'contains=.*(?: ).*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

#    
f = {' ': r'contains=.*.*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*.*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

f = {' ': r'contains=.*.*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[' '] = df_slice

#   
f = {' ': r'contains=.*'}
df_slice = df_filter_post_proc(ppp, f, rzpr_base, label_base)
lst[''] = df_slice

, , .

# ,      
for group in lst:
    el = lst[group]
    print(len(el['name']), group, 'ppp:', '|'.join(el['ppp']))
print('', len(ppp))

1  ppp: 308
11  ppp: 125|126|177|464|722|184|187|186|721|185
3   ppp: 160|171|388
23    ppp: 083|316|497|204|048|059|060|077|079|081|093|106|141|151|498|587|086|085|087|096
3  ppp: 258|259|730
2   ppp: 330|333
1   ppp: 305
20  ppp: 056|164|166|409|424|425|591|597|057|058|167|175|054
35    ppp: 075|139|190|226|319|386|401|423|486|494|573|589|677|693|073|074|144|385|595|384|007
3  ppp: 181|205|182
3  ppp: 163|166|167
7  ppp: 092|520|720|723|521|724|100
6 ,    ppp: 149|154|159|155|157|172
13 , , ,  ppp: 140|162|165|197|263|161|168|307|142|139|160
1  ppp: 153
6    ppp: 071|156|072|173|321
10    ppp: 020|099|129|306|021|023|101|143|022|725
7  ppp: 029|104|103|107|108|109|110|179
12  ppp: 078|089|128|134|397|201|070|084|088|135|071
0  ppp: 
6  ppp: 206|303|352|588|304
10 ,    ppp: 153|188|415|189|202|192|416|417|180
4  ppp: 434|436|437|438
1   ppp: 322
2  ppp: 318|320
4   ppp: 076|082|080|085
7 , , ,  ppp: 050|158|049|052|053|169|051
6  ppp: 133|279|132|309|360|069
7    ppp: 054|148|387|055|061|064|056|149
2  ppp: 777
1  ppp: 150
2      ppp: 392|393
1  ppp: 310
5   ppp: 022|350|340|370
5   ppp: 174|091|095|260|380
0   ppp: 
3  ppp: 152|302|090
 0

#           ,   
lst[' ']

{'name': ['   ',
  '    ',
  '      , ,   ,     ',
  '       ',
  '    '],
 'ppp': ['174', '091', '095', '260', '380'],
 'rzpr': ['0101', '0102', '0103', '0104', '0105', '0106', '0107'],
 'label': ['      ',
  'contains=.*(?:.*).*',
  'contains=.*(?:   ).*',
  'contains=.*(?:  .*).*']}

, .

#   ,             
df2 = groups_fill(df,lst)

# ,  
df2

2970 rows × 6 columns

#         
df3 = df2.loc[:,['(. .) ', 'src_filename', 'name']]
#  
df3.columns = ['value', 'year', 'name']

#  ,  
idx = df3[df3.loc[:,'value'] == ''].index

#  0
df3.loc[idx, 'value'] = '0.0'

#  float64
df3 = df3.astype({'value': 'float64'})

#       
df4 = df3.groupby(['year','name']).sum().reset_index()

#         
def manuscript(df):
    res = pd.DataFrame([])
    for i in df.index:
        n = df.loc[i,'name']
        y = df.loc[i,'year']
        v = df.loc[i,'value']
        res.loc[n,y] = v
    res['name'] = res.index
    #      
    last_idx = res.shape[1] - 1
    order = [last_idx]
    order = order + list(range(0, last_idx))
    res = res.iloc[:,order]
    return res

#       
df5 = manuscript(df4)
df5

#        d3.js    
def prep_data(df):
    lst = []
    size = df.shape[0]
    for i in range(0, size):
        row = df.iloc[i,:]
        name = row['name']

        #skip first column
        row_ = row[1:]
        for k, y in enumerate(row_.index):
            begin = float(row_[k]) # current year
            try:
                end = float(row_[k + 1]) # next year
            except:
                end = float(row_[k]) 

            range_ = end - begin
            step = range_ / 10

            cur = begin
            for n in range(0,10):
                last = cur
                cur = begin + step * (n+1)
                if cur < 0:
                    cur = 0.0
                lst.append({'name': name,
                            'value': round(cur, 2),
                            'year': float(str(y) + '.' + str(n)),
                            'lastValue': round(last, 2),
                            'rank': 0})

    df2 = pd.DataFrame(lst)
    df2 = df2.sort_values(by=['year','value'])
    df2.reset_index(drop=True, inplace = True)
    df2['rank'] = range(1,df2.shape[0]+1)
    return df2

#  
data = prep_data(df5)

data

6120行×5列

#    csv    d3.js
data.to_csv('data_groups.csv', index=False)

接下来，您需要将结果csv文件中的内容写入变量中的文件https://github.com/legale/d3.js-portable/blob/master/barchart-race-ppp-bundle2.htmlcsv_string。

我们看一下收到的比赛。比赛结果不言而喻。

此处为HTML：https：
//vneberu.ru/barchart-race2.html

这是youtube上的视频：

感谢您的关注，欢迎发表评论。

链接

可以在以下位置查看prepack库：https : //github.com/legale/prepack
可以从上面的链接下载收集的带有种族的包，但是您可以在此处。