熊猫在添加到新数据集时将数据转换为NaN

Question

I´ve been trying to extract specific data from a given data set and add it in a new one in a specific set of organized column. I'm doing this by reading a CSV file and using the string function. The problem is that even though the data is extracted correctly Pandas will add the second column as NaN even though there is data stored in the affected variable, please see my code below, any idea on how to fix this?

processor=pd.DataFrame()
Hospital_beds="SH.MED.BEDS.ZS"
Mask1=data["IndicatorCode"].str.contains(Hospital_beds)
stage=data[Mask1]
Hospital_Data=stage["Value"]
Birth_Rate="SP.DYN.CBRT.IN"
Mask=data["IndicatorCode"].str.contains(Birth_Rate)
stage=data[Mask]
Birth_Data=stage["Value"]
processor["Countries"]=stage["CountryCode"]
processor["Birth Rate per 1000 people"]=Birth_Data
processor["Hospital beds per 100 people"]=Hospital_Data
processor.head(10)

Answer 1

The problem here is that the indices are not matching up. When you initially populate the processor data frame you are using each line from the original dataframe that contained birth rate data. These lines are different from the ones that contain the hospital beds data so when you do

processor["Hospital beds per 100 people"] = Hospital_Data

pandas will create the new column, but since there are no matching indices for the Hospital_Data in processor it will just contain null values.

Probably what you first want to do is re-index the original data using the country code and the year

data.set_index(['CountryCode','Year'], inplace=True)

You can then create a view of just the indicators you are interested in

indicators = ['SH.MED.BEDS.ZS', 'SP.DYN.CBRT.IN'] dview = data[data.IndicatorCode.isin(indicators)]

Finally you can then pivot on the indicator code to view each indicator on the same line

dview.pivot(columns='IndicatorCode')['Value']

But note this will still contain a lot of NaNs. This is just because the hospital bed data is updated very infrequently (or eg in Aruba not at all). But you can filter these out as needed.