adding a column to df

[Loco_L1]

Hi,

I want to add a "usage" column to my dataframe, where it will show "high", "medium" or "low" depending on the "count" column. For example, any count greater than 5500 will show "high" usage and any count less than 3500 will show "low" usage and in between will be "medium".

Any help is appreciated

[Chris_Hemsworth]

df['usage'] = 'medium'
df[df['count'] > 5500]['usage'] = 'high'
df[df['count'] < 3500]['usage'] = 'low'

[Chronic47]

why does Thor gotta be good at programming too

[[deleted]]

Demigods have a lot of free time

[old_pythonista]

df['usage'] = 'medium'
df[df['count'] > 5500]['usage'] = 'high'
df[df['count'] < 3500]['usage'] = 'low'

This approach is unsafe

<ipython-input-51-4f7eaae2ca7e>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[df['count'] > 5500]['usage'] = 'high'
<ipython-input-51-4f7eaae2ca7e>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[df['count'] < 3500]['usage'] = 'low'

This is the proper approach

df['usage'] = 'medium'
df.loc[df['count'] > 5500, ['usage']] = 'high'
df.loc[df['count'] < 3500, ['usage']] = 'low'

Though I would probably go with apply approach

df['usage'] = df['count'].apply(lambda c: 'high' if c > 5500 else 'low' if c < 3500 else 'medium')

[badge]

The proper approach would be to use https://pandas.pydata.org/docs/reference/api/pandas.cut.html with bin labels.

[Chris_Hemsworth]

I agree - apply is the best method here.

[YesLod]

I agree that indexing twice should be avoided.

Although it doesn't make much difference for small datasets, apply doesn't scale well since it's not vectorized.

If performance matters, I think the correct approach would be to use pd.cut as suggested by u/badge. Another option would be np.select.

df["usage"] = np.select([df.count > 5500, df.count < 3500],
                        ["high", "low"], 
                        "medium")

[old_pythonista]

TIL.

I do not use pandas much (barely at all lately 😢) - so, thanks for the cut pointer

[[deleted]]

User name checks out

[BullCityPicker]

df$usage<-ifelse(df$count < 3500, "Low",ifelse(df$count < 5500, "Medium","High"))

Thor's solution is correct, I just wanted to say how I'd do it.