从 Pandas DF 的字符串列中提取数字

Question

我有下一个带有字符串列（“信息”）的 DataFrame：

df = pd.DataFrame( {'Date': ["2014/02/02", "2014/02/03"], 'Info': ["Out of 78 shares traded during the session today, there were 54 increases, 9 without change and 15 decreases.", "Out of 76 shares traded during the session today, there were 60 increases, 4 without change and 12 decreases."]})

我需要将“Info”中的数字提取到同一 df 中的新 4 列中。

第一行的值为 [78, 54, 9, 15]

我已经尝试过

df[["new1","new2","new3","new4"]]= df.Info.str.extract('(\d+(?:\.\d+)?)', expand=True).astype(int)

但我认为这更复杂。

问候，

Answer 1

Extractall 可能更适合这项任务

df[["new1","new2","new3","new4"]] = df['Info'].str.extractall(r'(\d+)')[0].unstack()

         Date                                               Info new1 new2 new3 new4
0  2014/02/02  Out of 78 shares traded during the session tod...   78   54    9   15
1  2014/02/03  Out of 76 shares traded during the session tod...   76   60    4   12

Answer 2

据我所知，您是在尝试避免捕获数字的小数部分，对吧？ （(?:\.\d+)? 部分。）

首先，如果你想要所有的匹配，你需要使用pd.Series.str.extractall； extract 在第一个之后停止。

使用你的df，试试这个代码：

# Get a multiindexed dataframe using extractall
expanded = df.Info.str.extractall(r"(\d+(?:\.\d+)?)")

# Pivot the index labels
df_2 = expanded.unstack()

# Drop the multiindex
df_2.columns = df_2.columns.droplevel()


# Add the columns to the original dataframe (inplace or make a new df)
df_combined = pd.concat([df, df_2], axis=1)