使用read_csv将财务数据导入Python Pandas

时间:2014-12-17 20:58:56

标签: python csv pandas import

我有一个具有以下结构的.csv:

date_begin,date_end,name,name_code,active_accounts,transaction_amount,transaction_count
1/1/2008,1/31/2008,Name_1,1001,"123,456","$7,890,123.45","67,890"
2/1/2008,2/29/2008,Name_1,1001,"43,210","$987,654.32","109,876"
3/1/2008,3/31/2008,Name_1,1001,"485,079","$1,265,789,433.98","777,888"
...
12/1/2008,12/31/2008,Name_1,1001,"87,543","$432,098,987","87,987"
1/1/2008,1/31/2008,Name_2,1002,"268,456","$890,123.45","97,890"
2/1/2008,2/29/2008,Name_2,1002,"53,210","$987,654.32","109,876"
...
etc

我正在尝试使用以下代码将它们读入pandas数据框:

import pandas as pd

data = pd.read_csv('my_awesome_csv.csv'),parse_dates=[[0,1]],
                   infer_datetime_format=True)

这很好用,除了我想控制每列中的数据类型。当我在解释器中运行以下代码时,我发现引号中的数字不会被识别为数字,无论是美元还是其他。

In [10]: data.dtypes
Out[10]: 
date_begin_date_end       object
name                      object
name_code                  int64
active_accounts           object  # Problem, I want this to be a number
transaction_amount        object  # Ditto, I want this to be a number (it's a dollar amount)
transaction_count         object  # Still a number!
dtype: object

我已经在Pandas csv documentation中做了一些窥探,但是在csv中用逗号和美元符号保存为字符串时,我没有找到关于声明类型的内容。我的最终目标是能够对这些列中的值进行一些算术运算。

有什么想法吗?

1 个答案:

答案 0 :(得分:10)

您可以在调用后使用vectorized string methods来解析这些列 read_csv

import pandas as pd
import decimal
D = decimal.Decimal

data = pd.read_csv('data', parse_dates=[[0,1]], infer_datetime_format=True)

for col in ('active_accounts', 'transaction_count'):
    data[col] = data[col].str.replace(r',', '').astype(int)

data['transaction_amount'] = (data['transaction_amount']
                              .str.replace(r'[^-+\d.]', '').astype(D))


print(data.dtypes)
# date_begin_date_end    object
# name                   object
# name_code               int64
# active_accounts         int64
# transaction_amount     object
# transaction_count       int64
# dtype: object

print(data)

产量

    date_begin_date_end    name  name_code  active_accounts  \
0    1/1/2008 1/31/2008  Name_1       1001           123456   
1    2/1/2008 2/29/2008  Name_1       1001            43210   
2    3/1/2008 3/31/2008  Name_1       1001           485079   
3  12/1/2008 12/31/2008  Name_1       1001            87543   
4    1/1/2008 1/31/2008  Name_2       1002           268456   
5    2/1/2008 2/29/2008  Name_2       1002            53210   

  transaction_amount  transaction_count  
0         7890123.45              67890  
1          987654.32             109876  
2      1265789433.98             777888  
3          432098987              87987  
4          890123.45              97890  
5          987654.32             109876  

PS。 read_csv确实有converters parameter 您可以使用它来提供解析有问题列的函数。这些 每个字符串调用一次函数。如果你有很多行,那可能需要很多Python函数 调用。使用矢量化字符串方法处理列,如上所示 应该快得多。

import pandas as pd
import re
import decimal
D = decimal.Decimal

def make_parser(cls):
    def parse_commas(text):
        return cls(re.sub(r'[^-+\d.]', '', text))
    return parse_commas

to_int = make_parser(int)
to_decimal = make_parser(D)

data = pd.read_csv('data', parse_dates=[[0,1]], infer_datetime_format=True
                   , converters={4: to_int, 5: to_decimal, 6: to_int})

print(data)

产量

    date_begin_date_end    name  name_code  active_accounts  \
0    1/1/2008 1/31/2008  Name_1       1001           123456   
1    2/1/2008 2/29/2008  Name_1       1001            43210   
2    3/1/2008 3/31/2008  Name_1       1001           485079   
3  12/1/2008 12/31/2008  Name_1       1001            87543   
4    1/1/2008 1/31/2008  Name_2       1002           268456   
5    2/1/2008 2/29/2008  Name_2       1002            53210   

  transaction_amount  transaction_count  
0         7890123.45              67890  
1          987654.32             109876  
2      1265789433.98             777888  
3          432098987              87987  
4          890123.45              97890  
5          987654.32             109876  

并且transaction_amount列中的值为dec​​imal.Decimals:

In [64]: data.loc[0, 'transaction_amount']
Out[64]: Decimal('7890123.45')