如何迭代一个 pandas df 来制作另一个 pandas df？

Question

Hi I have a dataframe df that has headers like this:

DATE    COL1    COL2   ...    COL10
date1    a       b      
...     ...     ...            ...

and so on        

Basically each row is just a date and then a bunch of columns on the same row that have some text in or they don't.

From this I want to create a new df df2 that has a row for each non blank 'cell' in the original data frame consisting of the date and the text from that cell. From the above example we could get

df2=

DATE    COL
date1    a
date1    b

In pseudocode what I want to achieve is:

df2 = blank df
for row in df:
    for column in row:
        if cell is not empty:
            append to df2 a row consisting of the date for that row and the value in that cell

So far I have

import pandas as pd
df = pd.read_csv("data2.csv")

output_df = pd.DataFrame(columns=['Date', 'Col'])

Basically I have read in the df, and created the new df to begin populating.

Now I am stuck, some investigation has told me I should not use iterrows() as it is not efficient and bad practise and I have 300k+ rows in df.

Any suggestions how I can do this please?

Answer 1

Use df.melt :

data = [{'date': f'date{j}', **{f"col{i}": val for i, val in enumerate('abc')}} for j in range(5)]

df = pd.DataFrame(data)

    date col0 col1 col2
0  date0    a    b    c
1  date1    a    b    c
2  date2    a    b    c
3  date3    a    b    c
4  date4    a    b    c


df2 = df.melt(
    id_vars=['date'], 
    value_vars=df.filter(like='col').columns, 
    value_name='Col'
)[['date', 'Col']]


# to get the ordering the way you want
df2 = df2.sort_values(by='date').reset_index(drop=True)
     date Col
0   date0   a
1   date0   b
2   date0   c
3   date1   a
4   date1   b
5   date1   c
6   date2   a
7   date2   b
8   date2   c
9   date3   a
10  date3   b
11  date3   c
12  date4   a
13  date4   b
14  date4   c

Then, you can filter out any null values from Col :

df2 = df2[df2['Col'].apply(bool)]

Answer 2

You need to turn the blank cells into NA.

ie

df[df == ''] = np.nan

df.metl('DATE').dropna()

Answer 3

You can join the multiple columns to one list

s = df.filter(like='COL').apply(lambda row: row[row.notna()].tolist(), axis=1)

Then explode on that list

df_ = pd.DataFrame({'DATE':df['DATE'], 'COL': s})
df_ = df_.explode('COL')

print(df_)

    DATE COL
0  date1   a
0  date1   b
1  date2   c
1  date2   d