I'm reindexing a dataframe in the standard way, i.e.
df.reindex(newIndex,method='ffill')
But realized I need to handle missing data differently on a column-by-column basis. That is, for some columns I want to ffill, but for others I want to missing values recorded as NAs.
For simplicity, let's say I have column X that I want ffilled, and column Y that I want NA-filled. How can I call .reindex to accomplish this?
You can reindex()
first, and then call ffill()
for columns:
import pandas as pd
df = pd.DataFrame({"A":[10, 20, 30], "B":[100, 200, 300],
"C":[100, 200, 300]}, index=[2, 6, 8])
df2 = df.reindex([2,4,6,8,10])
for col in ["A", "B"]:
df2[col].ffill(inplace=True)
print df2
output:
A B C
2 10 100 100
4 10 100 NaN
6 20 200 200
8 30 300 300
10 30 300 NaN
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