style pandas based on multiple conditions?

Question

I want to color some rows based on 2-3 conditions:

df

    status days_since_claim claim_action
0   Closed         349 days  No action       
1   Closed         353 days  No action           
2  Granted         373 days  Check account           
3  Granted         431 days  Account checked           
4   Closed         448 days  No action

I want to fill the background based on all three columns

i.e.

`backgroud_color: 'green' if 'status' == 'Closed' and claim_action == 'No action'

`backgroud_color: 'red' if 'status' == 'Granted' and claim_action == 'Check account' and 'days_since_claim' > 300`

I tried:

styled = mdf.style.applymap(lambda v: 'background-color: %s' %
                                      'red' if v > 300 else "")
def color_s(df):
    for i, row in df.iterrows():
        if row['status'] == 'Closed':
                 .
                 .

I don't think I am able to grasp the concept of how styling works. Can someone explain a bit with example?

Thanks in advance.

jezrael · Accepted Answer · 2020-01-31 13:02:01Z

5

You can create DataFrame of styles with Styler.apply and set rows by conditions with loc:

def color(x):
    c1 = 'background-color: green'
    c2 = 'background-color: red'
    c = '' 
    #compare columns
    mask1 = (x['status'] == 'Closed') & 
            (x['claim_action'] == 'No action')
    mask2 = (x['status'] == 'Granted') & 
            (x['claim_action'] == 'Check account') & 
            (x['days_since_claim'].dt.days > 300)
    #DataFrame with same index and columns names as original filled empty strings
    df1 =  pd.DataFrame(c, index=x.index, columns=x.columns)
    #modify values of df1 column by boolean mask
    df1.loc[mask1, :] = c1
    df1.loc[mask2, :] = c2
    return df1

df.style.apply(color, axis=None)

edited Jan 31, 2020 at 13:02

answered Jan 31, 2020 at 12:56

jezrael

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