python pandas - 使用 astype() 处理字符串中的逗号到浮点转换的通用方法

Question

是否有通用方法告诉 pandas 使用逗号 (",") 作为从字符串到浮点等类型转换的小数点分隔符？

import pandas as pd
from datetime import datetime

data = {
    "col_str": ["a", "b", "c"],
    "col_int": ["1", "2", "3"],
    "col_float": ["1,2", "3,2342", "97837,8277"],
    "col_float2": ["13,2", "3234,2342", "263,8277"],
    "col_date": [datetime(2020, 8, 1, 0, 3, 4).isoformat(),
                 datetime(2020, 8, 2, 2, 4, 5).isoformat(),
                 datetime(2020, 8, 3, 6, 8, 4).isoformat()
                 ]
}

conversion_dict = {
    "col_str": str,
    "col_int": int,
    "col_float": float,
    "col_float2": float,
    "col_date": "datetime64"
}

df = pd.DataFrame(data=data)

print(df.dtypes)
df = df.astype(conversion_dict, errors="ignore")
print(df.dtypes)
print(df)

上面的例子returns object-columns for "col_float" and "col_float2" or throws an error is errors set to "raise".

我想直接使用 astype() 方法，而无需手动将逗号替换为点。数据源通常 returns 以逗号作为小数分隔符浮动，因为语言环境设置为德语。

是否有一种通用的方法来告诉 pandas 浮点数中的逗号类型 - 或任何其他带小数的数字数据类型 - 是否可以并应自动转换？

PS：我不能在可以直接指定分隔符的地方使用 read_csv，因为它是一个数据库。

提前致谢。

Answer 1

您可以使用 locale 库通过 apply() 和 locale.atof 以通用方式解决此问题。只需替换为适当的语言环境即可。我在这种情况下使用 de_DE，因为他们使用“，”小数点。

import locale
from datetime import datetime

import pandas as pd

locale.setlocale(locale.LC_ALL, locale="de_DE")


data = {
    "col_str": ["a", "b", "c"],
    "col_int": ["1", "2", "3"],
    "col_float": ["1,2", "3,2342", "97837,8277"],
    "col_float2": ["13,2", "3234,2342", "263,8277"],
    "col_date": [datetime(2020, 8, 1, 0, 3, 4).isoformat(),
                 datetime(2020, 8, 2, 2, 4, 5).isoformat(),
                 datetime(2020, 8, 3, 6, 8, 4).isoformat()
                 ]
}

conversion_dict = {
    "col_str": str,
    "col_int": int,
    "col_float": str,
    "col_float2": str,
    "col_date": "datetime64"
}

df = pd.DataFrame(data=data)

print(df.dtypes)
df = df.astype(conversion_dict, errors="ignore")
df["col_float"] = df["col_float"].apply(locale.atof)
df["col_float2"] = df["col_float2"].apply(locale.atof)
print(df.dtypes)
print(df)

输出：

col_str       object
col_int       object
col_float     object
col_float2    object
col_date      object
dtype: object
col_str               object
col_int                int64
col_float            float64
col_float2           float64
col_date      datetime64[ns]
dtype: object
  col_str  col_int   col_float  col_float2            col_date
0       a        1      1.2000     13.2000 2020-08-01 00:03:04
1       b        2      3.2342   3234.2342 2020-08-02 02:04:05
2       c        3  97837.8277    263.8277 2020-08-03 06:08:04

Answer 2

我通过以下解决方法解决了这个问题。在某些情况下这仍然可能会中断，但我没有找到一种方法来告诉 pands astype() 逗号是可以的。如果有人有其他仅 pandas 的解决方案，请告诉我：

import locale
from datetime import datetime
import pandas as pd

data = {
    "col_str": ["a", "b", "c"],
    "col_int": ["1", "2", "3"],
    "col_float": ["1,2", "3,2342", "97837,8277"],
    "col_float2": ["13,2", "3234,2342", "263,8277"],
    "col_date": [datetime(2020, 8, 1, 0, 3, 4).isoformat(),
                 datetime(2020, 8, 2, 2, 4, 5).isoformat(),
                 datetime(2020, 8, 3, 6, 8, 4).isoformat()
                 ]
}

conversion_dict = {
    "col_str": str,
    "col_int": int,
    "col_float": float,
    "col_float2": float,
    "col_date": "datetime64"
}

df = pd.DataFrame(data=data)
throw_error = True

try:
    df = df.astype(conversion_dict, errors="raise")
except ValueError as e:
    error_message = str(e).strip().upper()
    error_search = "COULD NOT CONVERT STRING TO FLOAT:"
    # compare error messages to only get the string to float error because pandas only throws ValueError´s which
    # are not datatype specific. This might be quite hacky because error messages could change.
    if error_message[:len(error_search)] == error_search:
        # convert everything else and ignore errors for the float columns
        df = df.astype(conversion_dict, errors="ignore")
        # go over the conversion dict
        for key, value in conversion_dict.items():
            # print(str(key) + ":" + str(value) + ":" + str(df[key].dtype))
            # only apply to convert-to-float-columns which are not already in the correct pandas type float64
            # if you don´t check for correctly classified types, .str.replace() throws an error
            if (value == float or value == "float") and df[key].dtype != "float64":
                # df[key].apply(locale.atof) or anythin locale related is plattform dependant and therefore bad
                # in my opinion
                # locale settings for atof
                # WINDOWS: locale.setlocale(locale.LC_ALL, 'deu_deu')
                # UNIX: locale.setlocale(locale.LC_ALL, 'de_DE')
                df[key] = pd.to_numeric(df[key].str.replace(',', '.'))
    else:
        if throw_error:
            # or do whatever is best suited for your use case
            raise ValueError(str(e))
        else:
            df = df.astype(conversion_dict, errors="ignore")

print(df.dtypes)
print(df)

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