How do I compare the two data frames and get the values?

Question

The x data frame is information about departure and arrival, and the y data frame is latitude and longitude data for each location.

I try to calculate the distance between the origin and destination using the latitude and longitude data of start and end (eg, start_x, start_y, end_x, end_y).

How can I connect x and y to bring the latitude data that fits each code into the x data frame?

Answer 1

The notation is somewhat confusing, but I took it after the question's notation.

One way to do would be by merging your dataframes into a new one like so:

Dummy dataframes:

import pandas as pd

x=[300,500,300,600,700]
y=[400,400,700,700,400]

code=[300,400,500,600,700]

start=[100,101,102,103,104]

end=[110,111,112,113,114]

x={"x":x, "y":y}

y={"code":code, "start":start, "end":end}

x=pd.DataFrame(x)

y=pd.DataFrame(y)

This gives:

x

	x	y
0	300	400
1	500	400
2	300	700
3	600	700
4	700	400

y

	code	start	end
0	300	100	110
1	400	101	111
2	500	102	112
3	600	103	113
4	700	104	114

Solution:

df = pd.merge(x,y,left_on="x",right_on="code").drop("code",axis=1)

df

	x	y	start	end
0	300	400	100	110
1	300	700	100	110
2	500	400	102	112
3	600	700	103	113
4	700	400	104	114

df = df.merge(y,left_on="y",right_on="code").drop("code",axis=1)

df

	x	y	start_x	end_x	start_y	end_y
0	300	400	100	110	101	111
1	500	400	102	112	101	111
2	700	400	104	114	101	111
3	300	700	100	110	104	114
4	600	700	103	113	104	114

Quick explanation:

The line df = pd.merge(...) creates the new dataframe by merging the left one (x) on the "x" column and the right one on the "code" column. The second line df = df.merge(...) takes the existing df as the left one, and uses its column "y" to merge the "code" column from the y dataframe.

The.drop("code",axis=1) is used to drop the unwanted "code" column resulting from the merging.

The _x and _y suffixes are added automatically when merging dataframes that have the same column names. To control it, use the "suffixe=.." option when calling the second merging (when the column with the same name are merging). In this case it works right with the default setting so no bothering with this if you use the x as right and y as left dataframes.