問(wèn)題描述

我從列表列表中創(chuàng)建了一個(gè) pandas 數(shù)據(jù)框

I created a pandas dataframe from a list of lists

import pandas as pd

df_list = [["a", "1", "2"], ["b", "3", np.nan]]
df = pd.DataFrame(df_list, columns = list("ABC"))
>>>   A  B    C
   0  a  1    2
   1  b  3  NaN

有沒(méi)有辦法將數(shù)據(jù)框的所有列轉(zhuǎn)換為可以轉(zhuǎn)換的浮點(diǎn)數(shù)，即 B 和 C?如果您知道要轉(zhuǎn)換哪些列，則可以使用以下方法:

Is there a way to convert all columns of the dataframe to float, that can be converted, i.e. B and C? The following works, if you know, which columns to convert:

  df[["B", "C"]] = df[["B", "C"]].astype("float")

但是，如果您事先不知道哪些列包含數(shù)字，您會(huì)怎么做?當(dāng)我嘗試時(shí)

But what do you do, if you don't know in advance, which columns contain the numbers? When I tried

  df = df.astype("float", errors = "ignore")

所有列仍然是字符串/對(duì)象.同樣，

all columns are still strings/objects. Similarly,

df[["B", "C"]] = df[["B", "C"]].apply(pd.to_numeric)

轉(zhuǎn)換兩列(雖然B"是 int 而C"是float"，因?yàn)榇嬖?NaN 值)，但是

converts both columns (though "B" is int and "C" is "float", because of the NaN value being present), but

df = df.apply(pd.to_numeric)

顯然會(huì)引發(fā)錯(cuò)誤消息，我看不出有什么方法可以抑制它.
是否有可能在不遍歷每一列的情況下執(zhí)行此字符串-浮點(diǎn)轉(zhuǎn)換，以嘗試 .astype("float", errors = "ignore")?

obviously throws an error message and I don't see a way to suppress this.
Is there a possibility to perform this string-float conversion without looping through each column, to try .astype("float", errors = "ignore")?

推薦答案

我覺(jué)得你需要errors='ignore'pandas-docs/stable/generated/pandas.to_numeric.html" rel="noreferrer">to_numeric:

I think you need parameter errors='ignore' in to_numeric:

df = df.apply(pd.to_numeric, errors='ignore')
print (df.dtypes)
A     object
B      int64
C    float64
dtype: object

如果不是混合值，它工作得很好 - 帶有字符串的數(shù)字:

It working nice if not mixed values - numeric with strings:

df_list = [["a", "t", "2"], ["b", "3", np.nan]]
df = pd.DataFrame(df_list, columns = list("ABC"))

df = df.apply(pd.to_numeric, errors='ignore')
print (df)
   A  B    C
0  a  t  2.0 <=added t to column B for mixed values
1  b  3  NaN

print (df.dtypes)
A     object
B     object
C    float64
dtype: object

您也可以將 int 向下轉(zhuǎn)換為 floats:

You can downcast also int to floats:

df = df.apply(pd.to_numeric, errors='ignore', downcast='float')
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

同理:

df = df.apply(lambda x: pd.to_numeric(x, errors='ignore', downcast='float'))
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

這篇關(guān)于在所有 pandas 列中將字符串轉(zhuǎn)換為浮點(diǎn)數(shù)，這是可能的的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！