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Python 3.x 仅从数据帧中提取数字和字符串_Python 3.x_Pandas_Dataframe_Data Science_Text Extraction - Fatal编程技术网

Python 3.x 仅从数据帧中提取数字和字符串

Python 3.x 仅从数据帧中提取数字和字符串,python-3.x,pandas,dataframe,data-science,text-extraction,Python 3.x,Pandas,Dataframe,Data Science,Text Extraction,我试图只提取两个不同数据帧中的数字和字符串。我使用正则表达式来提取数字和字符串 import pandas as pd df_num = pd.DataFrame({ 'Colors': ['lila1.5', 'rosa2.5', 'gelb3.5', 'grün4', 'rot5', 'schwarz6', 'grau7', 'weiß8', 'braun9', 'hellblau10'], 'Animals': ['hu11nd', '12welpe',

我试图只提取两个不同数据帧中的数字和字符串。我使用正则表达式来提取数字和字符串

import pandas as pd

df_num = pd.DataFrame({
        'Colors': ['lila1.5', 'rosa2.5', 'gelb3.5', 'grün4', 'rot5', 'schwarz6', 'grau7', 'weiß8', 'braun9', 'hellblau10'],
        'Animals': ['hu11nd', '12welpe', '13katze', 's14chlange', 'vo15gel', '16papagei', 'ku17h', '18ziege', '19pferd',
                    'esel20']
    })

for column in df_num.columns:
    df_num[column] = df_num[column].str.extract('(\d+)').astype(float)

print(df_num)
我还尝试过使用
([\d+][\d+\.\d+])和“([\d+\.\d+])”

在这里,我得到了输出,但不是我所期望的。虽然我期望浮点数,但我没有得到1.5或2.5

我得到如下图片:

在这种情况下,当数字在结尾或开始时,我得到字符串,但是如果把数字放在中间或任何其他地方,那么结果是我期待的,我没有得到。 当前输出如下图所示:


我认为我的正则表达式不正确。哪个正则表达式将是解决这些问题的正确方法?或者有没有其他方法可以只提取数据帧中的数字和字符串?

您可以利用内置的
str
方法,或者不使用正则表达式。见下文:

# get rid of letters and handle floating points
>>> "".join([c for c in "word234with23numbers" if c.isnumeric() or c == "."])
"23423"

>>> "".join([c for c in "gelb3.5" if c.isnumeric() or c == "."])
"3.5"

# get rid of numbers
>>> "".join([c for c in "word234with23numbers" if c.isalpha()])
"wordwithnumbers"
您可以使用
(\d+\.\d+\d+
提取
您的数字,并
结果替换为
来获取字符串

print (df_num.assign(colors_num=df_num["Colors"].str.extract(r"(\d+\.\d+|\d+)"))
             .assign(colors_col=df_num["Colors"].str.replace(r"(\d+\.\d+|\d+)","")))

       Colors     Animals colors_num colors_col
0     lila1.5      hu11nd        1.5       lila
1     rosa2.5     12welpe        2.5       rosa
2     gelb3.5     13katze        3.5       gelb
3       grün4  s14chlange          4       grün
4        rot5     vo15gel          5        rot
5    schwarz6   16papagei          6    schwarz
6       grau7       ku17h          7       grau
7       weiß8     18ziege          8       weiß
8      braun9     19pferd          9      braun
9  hellblau10      esel20         10   hellblau

您的代码在正确的轨道上,您只需要考虑小数和整数的可能性:

df_num['colors_num'] = df_num.Colors.str.extract(r'(\d+[.\d]*)')
df_num['animals_num'] = df_num.Animals.str.extract(r'(\d+[.\d]*)')
df_num['colors_str'] = df_num.Colors.str.replace(r'(\d+[.\d]*)','')
df_num['animals_text'] = df_num.Animals.str.replace(r'(\d+[.\d]*)','')


    Colors  Animals colors_num  animals_num colors_str  animals_text
0   lila1.5 hu11nd  1.5 11  lila    hund
1   rosa2.5 12welpe 2.5 12  rosa    welpe
2   gelb3.5 13katze 3.5 13  gelb    katze
3   grün4   s14chlange  4   14  grün    schlange
4   rot5    vo15gel 5   15  rot vogel
5   schwarz6    16papagei   6   16  schwarz papagei
6   grau7   ku17h   7   17  grau    kuh
7   weiß8   18ziege 8   18  weiß    ziege
8   braun9  19pferd 9   19  braun   pferd
9   hellblau10  esel20  10  20  hellblau    esel

最简单的方法是定义一些函数:

def text(x):
    return x.str.replace(r'[0-9.]+','')
def values(x):
    return x.str.extract(r'([0-9.]+)', expand = False)

df_str.transform([text,values])

          Colors          Animals       
       text values      text values
0      lila    1.5      hund     11
1      rosa    2.5     welpe     12
2      gelb      3     katze     13
3      grün      4  schlange     14
4       rot      5     vogel     15
5   schwarz      6   papagei     16
6      grau      7       kuh     17
7      weiß      8     ziege     18
8     braun      9     pferd     19
9  hellblau     10      esel     20

这回答了你的问题吗?
def text(x):
    return x.str.replace(r'[0-9.]+','')
def values(x):
    return x.str.extract(r'([0-9.]+)', expand = False)

df_str.transform([text,values])

          Colors          Animals       
       text values      text values
0      lila    1.5      hund     11
1      rosa    2.5     welpe     12
2      gelb      3     katze     13
3      grün      4  schlange     14
4       rot      5     vogel     15
5   schwarz      6   papagei     16
6      grau      7       kuh     17
7      weiß      8     ziege     18
8     braun      9     pferd     19
9  hellblau     10      esel     20