I am a new user to Pandas and I love it!
I am trying to create a pivot table in Pandas. Once I have pivot table the way I want, I would like to rank the values by the columns.
I've attached an image from Excel as it is easier to see in tabular format what I am trying to achieve. Link to image
I've searched through stackoverflow but am having trouble finding an answer. I tried using .sort() but this doesn't work. Any help will be appreciated.
Thanks in advance
This should do what you are looking for:
In [1]: df = pd.DataFrame.from_dict([{'Country': 'A', 'Year':2012, 'Value': 20, 'Volume': 1}, {'Country': 'B', 'Year':2012, 'Value': 100, 'Volume': 2}, {'Country': 'C', 'Year':2013, 'Value': 40, 'Volume': 4}])
In [2]: df_pivot = pd.pivot_table(df, index=['Country'], columns = ['Year'],values=['Value'], fill_value=0)
In [3]: df_pivot
Out [4]:
Value
Year 2012 2013
Country
A 20 0
B 100 0
C 0 40
In [5]: df = df_pivot.reindex(df_pivot['Value'].sort_values(by=2012, ascending=False).index)
Out [6]:
Value
Year 2012 2013
Country
B 100 0
A 20 0
C 0 40
Basically it gets the index of the sorted values and reindex the initial pivot table.
df_pivot.reindex, not df.you can sort on more than one column in the pivot table. In my case, I have the probability of accident at postcode and probability of accident at address to sort descending and display the results in a heatmap.
pivot = df.pivot_table(index=['postcode'],values=['probability_at_address','probability_at_postcode'],aggfunc='mean').sort_values(by=['probability_at_address','probability_at_postcode'],ascending=False)
fig,ax=plt.subplots(figsize=(10,20))
sns.heatmap(pivot,cmap="Blues",ax=ax)
plt.show()
