 # Demographic data - What country has the highest percentage of people that earn >50K?

Tell us what’s happening:
I am getting the result as United States with a percent of 91.4 - which is the incorrect answer.

# What country has the highest percentage of people that earn >50K?

``````totalcount=df[df['salary']=='>50K'].count().iloc
df1 = df[df['salary']=='>50K'].groupby('native-country',as_index=False).size()
df1.columns=['country','50Kcount']
df1['percentcount']=df1['50Kcount']/totalcount*100
``````

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Challenge: Demographic Data Analyzer

Actually, what you’re checking is the percentage of people within people with more than `50K` only… (That’s what I’ve done first)…

but actually we need to calculate the percentage of people earning `>50K` within the country…
Hope you understood…

Won’t the answer still be the United States ? Since the number of rich earners is the highest in USA?

In the above snippet, `rich_countries` is value_counts of series containing People earning more than 50K…

`countries` represent value counts of series of people,

Percent is calculated by `rich_countries*100/countries`

As for the US, Rich people are 7171, all entries are 29170, `Percentage = 24.58`(Percentage of 7171 people among 29170),

From the last cell, it’s clear that Iran has higher percentage of Rich People than US(From the dataset)

thank you for the explanation… Let me try to implement that now.

hi,
this is the exact point where I am stuck - how to calculate
I have a df called MT which is like countries.head() and another called MT2 which is like rich_countries.head().
this is the code I am working on:

``````MT = df[df['salary'] == '>50K'].groupby('native-country')['native-country'].count().reset_index(name="Total")

MT2 = df.groupby(["native-country"]).size().reset_index(name="Tot")

MT['percentage'] = np.where(MT['native-country'] == MT2['native-country'], MT['Total']/MT2['tot'],)

``````

any ideas on how to get it to calculate?
thanks and kind regards