如何将 pandas dataframe 从同名的多行转换为单列？

Question

I have tried to convert multiple rows with same name into single column but did not get exactly. I have done code for single and its mentioned in the following.

import pandas as pd 
url = 'https://raw.githubusercontent.com/gambler2020/Data_Analysis/master/population/population.csv'
df=pd.read_csv(url)
df

Image of dataframe The above code used to read and see the data frame from github.But the problem is that there are various countries and each country has hundreds of rows and i want to convert those each country having various rows into single column. The following code is for converting single countries with various rows into one column.

dff=df["Year"].head(259)
dff
a1=df[df["Entity"]=="Paraguay"]
a1=a1.rename(columns={"Population (historical estimates)": "Paraguay"})
a1=a1["Paraguay"]
a1=a1.reset_index(drop=True)
a1
dff=pd.concat([dff, a1], axis=1)
dff

image of output result This image is the sample of desired dataframe but it will take time to do for each countries because there are hundreds of countries. How do i write code so that i could convert raw dataframe image into desired output image for all the countries.

Answer 1

pandas.DataFrame.pivot

result = df.pivot(index='Year', columns='Entity', values='Population (historical estimates)').reset_index()
result.columns.name = None
result

###
      Year  Afghanistan        Africa    Albania     Algeria  American Samoa  …
0   -10000      14737.0  2.276110e+05     1199.0     12090.0             NaN  …
1    -9000      20405.0  3.230350e+05     1999.0     20150.0             NaN  … 
2    -8000      28253.0  4.629670e+05     3332.0     33583.0             NaN  … 
3    -7000      39120.0  6.700190e+05     5554.0     55973.0             NaN  … 
4    -6000      54166.0  9.791040e+05     9256.0     93289.0             NaN  … 
..     ...          ...           ...        ...         ...             ...   
254   2017   36296111.0  1.244222e+09  2884169.0  41389174.0         55617.0  …   
255   2018   37171922.0  1.275921e+09  2882735.0  42228415.0         55461.0  … 
256   2019   38041757.0  1.308064e+09  2880913.0  43053054.0         55312.0  …

[259 rows x 245 columns]