如何从熊猫数据框列中提取信息

Question

I have the dataframe below and I want to extract some information from column A and then create other columns to add them based on their types. Below is an example to illustrate this.

In [0]: df
Out[0]: 
          A                  
0 1258GA 25/01/20 TABLE 090626  038272
1 GOODIES 762088 A714816
2 TABLE AA88547 734963 GOODIES
3 WATER 02/450 FROM TOMORROW 48246
4 02H12 ALSCA 00548246B GOODIES

And I want to have the result below.

In [1]: df
Out[1]: 
          A                               Category             Date      Hour
0 1258GA 25/01/20 TABLE 090626  038272    TABLE           25/01/20
1 GOODIES 762088 A714816                  GOODIES 
2 TABLE AA88547 734963 GOODIES            TABLE GOODIES
3 WATER 02/450 FROM TOMORROW 48246        WATER 
4 02H12 ALSCA 00548246B GOODIES           GOODIES                        02H12

I've tried many things but haven't got that result

Answer 1

Maybe this helps:

df['A'].str.findall(r'\b[A-Z]+\b').str.join(' ')

0                  TABLE
1                GOODIES
2          TABLE GOODIES
3    WATER FROM TOMORROW
4          ALSCA GOODIES

Answer 2

You can certainly do that using Series.str methods,

The Series.str.extract() returns:

Extract capture groups in the regex pat as columns in a DataFrame.

For each subject string in the Series, extract groups from the first match of regular expression pat.

The Series.str.findall() returns:

Find all occurrences of pattern or regular expression in the Series/Index.

Here is the code snippet,

EDIT:

df["Category"] = df['A'].str.findall(r"(\b[A-Za-z]+\b)").str.join(' ')
df["Date"] = df['A'].str.extract(r"(\b[0-9]+/[0-9]+/[0-9]+\b)")
df["Hour"] = df['A'].str.extract(r"(\b[0-9]+H[0-9]+\b)")

And output will be,

                                      A             Category      Date   Hour
0  1258GA 25/01/20 TABLE 090626  038272                TABLE  25/01/20    NaN
1                GOODIES 762088 A714816              GOODIES       NaN    NaN
2          TABLE AA88547 734963 GOODIES        TABLE GOODIES       NaN    NaN
3      WATER 02/450 FROM TOMORROW 48246  WATER FROM TOMORROW       NaN    NaN
4         02H12 ALSCA 00548246B GOODIES        ALSCA GOODIES       NaN  02H12