CODEWITHSUNDEEP
pythonpandasdataframe
PrertoQuebas
PrertoQuebas

Reputation: 147

Edit all cells of column in Pandas DataFrame conditionally using original value of each cell

I have to edit all cells of one column (here column named "Links") in Pandas DataFrame conditionally using original value of each cell.

I know how to modify each cell of column, but don't know how to edit cell using original value of cell and make modification conditionally.

I have simple sample of Data Frame:

enter image description here

I am interested in last column "Links".

If cell endwswith .html I need to change it to:

<A> <original value> </A>

for example:

/l/sf-49ers/456346aaa.html

if it is number I need to make:

some-domain.info/number

for example:

some-domain.info/343

If it is text (string):

I need to put it in B tags:

for example:

"Baltimore Rayens"

If it is None I need to replace it with text "No link specified".

I have used this syntax:

def change_links(df):
    conditions = (..........)
    values = [.......................]
    df['Links'] = np.select(conditions, values)
    return df

but this does not work for me.

Upvotes: 1

Views: 1312

Answers (3)

TheFaultInOurStars
TheFaultInOurStars

Reputation: 3608

Since your question is kinda messy, I am answering your question based on what I have understood. You have two options in front of you:

  • apply
  • map

in either cases you simply need to do something like what follows:

def myCustomFunc(valueOfRow):
  # you need to change the value inside this function.
  return valueOfRow + "/more-link"
df["Link"] = df["Link"].apply(myCustomFunc)

If you are interested in map, you can use map function instead of apply in the abovementioned cell.

Upvotes: 0

Роберт Лорян
Роберт Лорян

Reputation: 13

As I understand you correct, you need to use apply function:

def change_links(link):
    if 'html' in link:
        newLink=0 #here change your link
    elif link[-1].isdigit():
        newLink='some-domain.info/'+str(link) #or do what you nedd
    else:
        newLink=1 #here add your tags
    return newLink
df['newLink'] = df['Links'].apply(lambda x(change_links(x)))

Upvotes: 1

Titouan L
Titouan L

Reputation: 1173

Theres a formatting probleme in your question, but you can achieve what you want with the function apply()

def myFunction(value):
    # For example for a int
    new_value = "some-domain.info/" + str(value)

    return new_value

df['Links'] = df.['Links'].apply(myFunction)

I could complete the answer with more information.

Upvotes: 0

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