Merging DataFrames within a loop [duplicate] - python-2.7

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Pandas Merging 101
(8 answers)
Closed 4 years ago.
I have a folder with numerous csv files which look like this:
csv1
2006 Percent Land_Use
0 13 5.379564 Developed
1 8 25.781580 Grass/Pasture
2 4 54.265050 Crop
3 15 0.363983 Water
4 16 6.244104 Wetlands
5 6 4.691764 Forest
6 1 3.031494 Alfalfa
7 11 0.137424 Shrubland
8 5 0.003671 Vetch
9 3 0.055412 Barren
10 7 0.009531 Grass
11 12 0.036423 Tree
csv2
2007 Percent Land_Use
0 13 2.742430 Developed
1 4 56.007242 Crop
2 8 24.227963 Grass/Pasture
3 16 8.839979 Wetlands
4 6 6.181062 Forest
5 1 1.446668 Alfalfa
6 15 0.366116 Water
7 3 0.127760 Barren
8 11 0.034426 Shrubland
9 7 0.000827 Grass
10 12 0.025528 Tree
csv3
2008 Percent Land_Use
0 13 1.863809 Developed
1 8 31.455578 Grass/Pasture
2 4 57.896856 Crop
3 16 2.693929 Wetlands
4 6 4.417966 Forest
5 1 1.239176 Alfalfa
6 7 0.130849 Grass
7 15 0.266536 Water
8 11 0.004571 Shrubland
9 3 0.030731 Barren
and I want to merge them all together into one DataFrame on Land_Use
I am reading in the files like this:
pth = (r'G:\')
for f in os.listdir(pth):
df=pd.read_csv(os.path.join(pth,f)
but I can't figure out how to merge all the individual dataframes after that. I figured out how to concat them but that isn't what I want. The type of merge I want is outer.
If I were to use a pathway to each csv file I would merge them like this, but I do NOT want to set a pathway to each file as there are many of them:
one=pd.read_csv(r'G:\one.csv')
two=pd.read_csv(r'G:\two.csv')
three=pd.read_csv(r'G:\three.csv')
merge=pd.merge(one,two, on=['Land_Use'], how='outer')
mergetwo=pd.merge(merge,three,on=['Land_Use'], how='outer')

Answer 1

I think you can use in python 3:
import functools
dfs = [df1,df2,df3]
df = functools.reduce(lambda left,right: pd.merge(left,right,on='Land_Use',how='outer'),dfs)
print (df)
2006 Percent_x Land_Use 2007 Percent_y 2008 Percent
0 13 5.379564 Developed 13.0 2.742430 13.0 1.863809
1 8 25.781580 Grass/Pasture 8.0 24.227963 8.0 31.455578
2 4 54.265050 Crop 4.0 56.007242 4.0 57.896856
3 15 0.363983 Water 15.0 0.366116 15.0 0.266536
4 16 6.244104 Wetlands 16.0 8.839979 16.0 2.693929
5 6 4.691764 Forest 6.0 6.181062 6.0 4.417966
6 1 3.031494 Alfalfa 1.0 1.446668 1.0 1.239176
7 11 0.137424 Shrubland 11.0 0.034426 11.0 0.004571
8 5 0.003671 Vetch NaN NaN NaN NaN
9 3 0.055412 Barren 3.0 0.127760 3.0 0.030731
10 7 0.009531 Grass 7.0 0.000827 7.0 0.130849
11 12 0.036423 Tree 12.0 0.025528 NaN NaN
In python 2:
df = reduce(lambda left,right: pd.merge(left,right,on='Land_Use',how='outer'),dfs)
Working solution with glob:
import pandas as pd
import functools
import glob
pth = 'a/*.csv'
files = glob.glob(pth)
dfs = [pd.read_csv(f, sep=';') for f in files]
df = functools.reduce(lambda left,right: pd.merge(left,right,on='Land_Use', how='outer'),dfs)
print (df)
2006 Percent_x Land_Use 2008 Percent_y 2007 Percent
0 13 5.379564 Developed 13.0 1.863809 13.0 2.742430
1 8 25.781580 Grass/Pasture 8.0 31.455578 8.0 24.227963
2 4 54.265050 Crop 4.0 57.896856 4.0 56.007242
3 15 0.363983 Water 15.0 0.266536 15.0 0.366116
4 16 6.244104 Wetlands 16.0 2.693929 16.0 8.839979
5 6 4.691764 Forest 6.0 4.417966 6.0 6.181062
6 1 3.031494 Alfalfa 1.0 1.239176 1.0 1.446668
7 11 0.137424 Shrubland 11.0 0.004571 11.0 0.034426
8 5 0.003671 Vetch NaN NaN NaN NaN
9 3 0.055412 Barren 3.0 0.030731 3.0 0.127760
10 7 0.009531 Grass 7.0 0.130849 7.0 0.000827
11 12 0.036423 Tree NaN NaN 12.0 0.025528

Answer 2

I am not allowed to comment, so I am unsure of what you exactly want.
You can try using one.merge(two, on=['Land_Use'], how='outer').merge(three,on=['Land_Use'], how='outer'). Let me know if you wanted something else.
If you have many dataframes, you can try using the reduce function. First create a list containing all the dataframes dataframes = [one, two, three, four, ... , twenty] You can add them into the list by using list comprehensions or by appending them into the list in your loop.
Then if you want to combine them based on Land_Use, you can use df_final = reduce(lambda left,right: pd.merge(left,right,on=['Land_Use'], how='outer'), dataframes)
Note: The reduce function is in the functools package in python 3+