I have a data frame with a column 'score'. It contains scores from 1 to 10. I want to create a new column "color" which gives the column color according to the score.
For e.g. if the score is 1, the value of color should be "#75968f", if the score is 2, the value of color should be "#a5bab7". i.e. we need colors ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2","#f1d4Af", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"] for scores [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] respectively.
Is it possible to do this without using a loop? Let me know in case you have a problem understanding the question.
Use Series.map with dictionary generated by zipping both lists or if need range by length of list colors is possible use enumerate:
df = pd.DataFrame({'score':[2,4,6,3,8]})
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2","#f1d4Af",
"#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
scores = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
df['new'] = df['score'].map(dict(zip(scores, colors)))
df['new1'] = df['score'].map(dict(enumerate(colors, 1)))
print (df)
score new new1
0 2 #a5bab7 #a5bab7
1 4 #e2e2e2 #e2e2e2
2 6 #dfccce #dfccce
3 3 #c9d9d3 #c9d9d3
4 8 #cc7878 #cc7878