如何使用python pandas从未命名列excel中过滤包含关键字的文本数据并打印到txt文件

Question

Im pretty new to this so please bear with me.

I have an excel sheet that contains certain text strings i would like to extract and copy to a text file - i have been doing this manually for a long time and im sick of it.

So my plan was to write a script that would extract this data from the excel sheet and create a txt file.

This is how far i have gotten:

#EXTRACT CLIPID FROM XCEL SHEET
import pandas as pd
from tkinter import Tk     # from tkinter import Tk for Python 3.x
from tkinter.filedialog import askopenfilename

Tk().withdraw() 
filename = askopenfilename()
data = pd.read_excel (filename)
df = pd.DataFrame(data)
print (df)

The data i want is located in column A1, but is not always in the same row. There are 3 separate keywords i want to look for:

1. "POP"
2. "TVS"
3. "PLANET"

The strings look something like this:

Channel2021_1_DRU_POP_15s_16062021 Channel2021_2_FANT_POP_15s_16062021 Channel2021_3_ITA_POP_15s_16062021

Channel2021_1_DRU_TVS_15s_16062021 Channel2021_2_FANT_TVS_15s_16062021 Channel2021_3_ITA_TVS_15s_16062021

Channel2021_1_DRU_PLANET_15s_16062021 Channel2021_2_FANT_PLANET_15s_16062021 Channel2021_3_ITA_PLANET_15s_16062021

This is the form of the extracted data i would like to write in a txt file.

So in essence i want to search column A1 for strings containing POP and print, then strings containing TVS and print, and lastly strings containing PLANET and print.

Any help would be greatly appreciated!

Thank you!

Dusan

PS: Here is the output of df :

                                         Unnamed: 0  ...                                        Unnamed: 16
0                                               NaN  ...                                                NaN
1                                               NaN  ...                                                NaN
2                                       Spot 1 15 s  ...                                                NaN
3                                               NaN  ...                                        Indicazioni
4                                         106290.01  ...                        dire tutto + grafica ITALIA
5                                         138575.01  ...                                                NaN
6                                         142956.01  ...                                                NaN
7                                          85146.01  ...                                                NaN
8      Eurospin2021_16bis_1_POP_ITA_15s_24_06_2021   ...                                                NaN
9       Eurospin2021_16bis_1_TVS_ITA_15s_24_06_2021  ...                                                NaN
10   Eurospin2021_16bis_1_PLANET_ITA_15s_24_06_2021  ...                                                NaN
11                                              NaN  ...                                                NaN
12                                              NaN  ...                                                NaN
13                                      Spot 2 15 s  ...                                                NaN
14                                              NaN  ...                                        Indicazioni
15                                        164171.01  ...                       dire tutto +  grafica ITALIA
16                                       9003309.01  ...                                                NaN
17                                         88310.01  ...                                                NaN
18      Eurospin2021_16bis_2_POP_ITA_15s_24_06_2021  ...                                                NaN
19      Eurospin2021_16bis_2_TVS_ITA_15s_24_06_2021  ...                                                NaN
20   Eurospin2021_16bis_2_PLANET_ITA_15s_24_06_2021  ...                                                NaN
21                                              NaN  ...                                                NaN
22                                              NaN  ...                                                NaN
23                                      Spot 3 15 s  ...                                                NaN
24                                              NaN  ...                                         Istruzione
25                                        800214.01  ...  dire tutto + dire al kg dopo il prezzo per la ...
26                                       9001392.01  ...                                                NaN
27                                       9002306.01  ...                                                NaN
28                                        147804.01  ...                                                NaN
29     Eurospin2021_16bis_3_POP_DRUZ_15s_24_06_2021  ...                                                NaN
30     Eurospin2021_16bis_3_TVS_DRUZ_15s_24_06_2021  ...                                                NaN
31  Eurospin2021_16bis_3_PLANET_DRUZ_15s_24_06_2021  ...                                                NaN

[32 rows x 17 columns]

Answer 1

Here's a proposal if you're still looking for a solution:

Withe sample frame

df = pd.DataFrame({
    0: [
        'Channel2021_1_DRU_POP_15s_16062021',
        'Channel2021_2_FANT_POP_15s_16062021',
        'Channel2021_3_ITA_POP_15s_16062021',
        1.,
        2.,
        'Channel2021_1_DRU_TVS_15s_16062021',
        'Channel2021_2_FANT_TVS_15s_16062021',
        'Channel2021_3_ITA_TVS_15s_16062021',
        3.,
        4.,
        'Channel2021_1_DRU_PLANET_15s_16062021',
        'Channel2021_2_FANT_PLANET_15s_16062021',
        'Channel2021_3_ITA_PLANET_15s_16062021',
        5.
    ],
    1: '...',
})

                                         0    1
0       Channel2021_1_DRU_POP_15s_16062021  ...
1      Channel2021_2_FANT_POP_15s_16062021  ...
2       Channel2021_3_ITA_POP_15s_16062021  ...
3                                        1  ...
4                                        2  ...
5       Channel2021_1_DRU_TVS_15s_16062021  ...
6      Channel2021_2_FANT_TVS_15s_16062021  ...
7       Channel2021_3_ITA_TVS_15s_16062021  ...
8                                        3  ...
9                                        4  ...
10   Channel2021_1_DRU_PLANET_15s_16062021  ...
11  Channel2021_2_FANT_PLANET_15s_16062021  ...
12   Channel2021_3_ITA_PLANET_15s_16062021  ...
13                                       5  ...

this

selection = df.iloc[:, 0].str.contains(r'POP|TVS|PLANET', na=False)
print(df.iloc[:, 0][selection])
df.iloc[:, 0][selection].to_csv('items.txt', index=False, header=False)

prints you the desired entries

0         Channel2021_1_DRU_POP_15s_16062021
1        Channel2021_2_FANT_POP_15s_16062021
2         Channel2021_3_ITA_POP_15s_16062021
5         Channel2021_1_DRU_TVS_15s_16062021
6        Channel2021_2_FANT_TVS_15s_16062021
7         Channel2021_3_ITA_TVS_15s_16062021
10     Channel2021_1_DRU_PLANET_15s_16062021
11    Channel2021_2_FANT_PLANET_15s_16062021
12     Channel2021_3_ITA_PLANET_15s_16062021

and writes them into a file items.txt

Channel2021_1_DRU_POP_15s_16062021
Channel2021_2_FANT_POP_15s_16062021
Channel2021_3_ITA_POP_15s_16062021
Channel2021_1_DRU_TVS_15s_16062021
Channel2021_2_FANT_TVS_15s_16062021
Channel2021_3_ITA_TVS_15s_16062021
Channel2021_1_DRU_PLANET_15s_16062021
Channel2021_2_FANT_PLANET_15s_16062021
Channel2021_3_ITA_PLANET_15s_16062021

Since I'm unsure about the column names I have only used the index base selection ( .iloc ).

If you want the results in the order you've given then this

df = pd.concat([
         df.iloc[:, 0][df.iloc[:, 0].str.contains(tag, na=False)]
         for tag in ('POP', 'TVS', 'PLANET')
     ])

should work (just print df afterwards or write it to a file).

Btw.: This is too complicated

data = pd.read_excel (filename)
df = pd.DataFrame(data)

You only need pd.read_excel :

df = pd.read_excel(filename)

EDIT : Regarding the comments:

with open('items.txt', 'wt') as file:
    file.write('The following has been sent:')
    for tag in ('POP', 'TVS', 'PLANET'):
        file.write(f'\n{tag}:\n')
        items = df.iloc[:, 0][df.iloc[:, 0].str.contains(tag, na=False)].to_list()
        file.write('\n'.join(items))