Changing value of data in pandas using if-CodePudding

I have a csv file that I need to change some values in based on 2 other values. Normally i would use if statements but as we all know pandas is not that easy to do that with.

but how can I now change Cat_Tier_2 to wireless or corded etc based on the word wireless or corded in the description for that line only?

Name                                                      x brand mouse
Description                                               I am a wireless mouse
Cat_Tier_1                                                Peripherals
Cat_Tier_2                                                Mouse

So I can change Cat_Tier_1 to mouse by

df["Cat_Tier_1"] = df["Cat_Tier_1"].str.replace("Peripherals", "Mouse")

using pandas I cant use the good old if statements e.g

if "wireless" in df["Description"]:
  df["Cat_Tier_2"].str.replace("Mouse", "Wireless")
elif "corded" in df["Description"]:
  df["Cat_Tier_2"].str.replace("Mouse", "Corded")
else:
  pass

CodePudding user response：

This will do what your question asks:

df.loc[df.Description.str.contains('wireless'), 'Cat_Tier_2'] = 'wireless'
df.loc[df.Description.str.contains('corded'), 'Cat_Tier_2'] = 'corded'

Input:

            Name            Description   Cat_Tier_1 Cat_Tier_2
0  x brand mouse  I am a wireless mouse  Peripherals      Mouse

Output:

            Name            Description   Cat_Tier_1 Cat_Tier_2
0  x brand mouse  I am a wireless mouse  Peripherals   wireless

CodePudding user response：

Use Series.str.contains for filter only matched rows by condition in DataFrame.loc if need replace substring:

m1 = df["Description"].str.contains('wireless')
m2 = df["Description"].str.contains('corded')

df.loc[m1, "Cat_Tier_2"] = df.loc[m1, "Cat_Tier_2"].str.replace("Mouse", "Wireless")
df.loc[m2, "Cat_Tier_2"] = df.loc[m2, "Cat_Tier_2"].str.replace("Mouse", "Corded")

CodePudding user response：

There are multiple ways you can deal with that.

If you still want to use your if-else or maybe in the future you want to build complex logic to apply to columns one of the ways is pandas apply where you can pass columns and functions you want to apply, for example:

def func(value):
  new_val = None
  if "wireless" in value:
    new_val = value.replace("Mouse", "Wireless")
  elif "corded" in value:
    new_val = value.replace("Mouse", "Corded")
  else:
    new_val = value

  return new_val

df['Cat_Tier_2'] = df['Description'].apply(func)

but you need this if you have very complex logic. This problem can be fixed with a more simple approach with loc like jezrael supposed.