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
