Consider the following pandas.DataFrame:
>>> import pandas as pd >>> df = pd.DataFrame({ ... "sym": ["a", "b", "c"], ... "del": [1, 2, 3] ... }) And consider the following dict:
>>> d = [{"sid": 99, "sym": "b"}, {"sid": 88, "sym": "c"}] I need to update df's index with the value of sid where sym matches. For this example, my output would like like this:
>>> df sym del 0 a 1 99 b 2 88 c 3 How might I do this?
Use Series.map with dictionary, then replace missing values by original index values:
d = [{"sid": 99, "sym": "b"}, {"sid": 88, "sym": "c"}] d1 = {x['sym']:x['sid'] for x in d} df.index = df['sym'].map(d1).fillna(df.index.to_series()).astype(int).rename(None) print (df) sym del 0 a 1 99 b 2 88 c 3 df = pd.DataFrame({ "sym": ["a", "b", "c"], "del": [1, 2, 3] }, index=[50,51,52]) print (df) sym del 50 a 1 51 b 2 52 c 3 d = [{"sid": 99, "sym": "b"}, {"sid": 88, "sym": "c"}] d1 = {x['sym']:x['sid'] for x in d} df.index = df['sym'].map(d1).fillna(df.index.to_series()).astype(int).rename(None) print (df) sym del 50 a 1 99 b 2 88 c 3 
here is one way using merge after converting the list of dict to a dataframe:
m = df.merge(pd.DataFrame(d),on='sym',how='left') df.index = m['sid'].fillna(df.index.to_series()).astype(int).rename(None) print(df) sym del 0 a 1 99 b 2 88 c 3 