在python中使用pandas将csv文件附加到一个

Question

I have n files in a directory that I need to combine into one. They have the same amount of columns, for example, the contents of test1.csv are:

test1,test1,test1  
test1,test1,test1  
test1,test1,test1  

Similarly, the contents of test2.csv are:

test2,test2,test2  
test2,test2,test2  
test2,test2,test2  

I want final.csv to look like this:

test1,test1,test1  
test1,test1,test1  
test1,test1,test1  
test2,test2,test2  
test2,test2,test2  
test2,test2,test2  

But instead it comes out like this:

test file 1,test file 1.1,test file 1.2,test file 2,test file 2.1,test file 2.2  
,,,test file 2,test file 2,test file 2  
,,,test file 2,test file 2,test file 2  
test file 1,test file 1,test file 1,,,  
test file 1,test file 1,test file 1,,,  

Can someone help me figure out what is going on here? I have pasted my code below:

import csv
import glob
import pandas as pd
import numpy as np 

all_data = pd.DataFrame() #initializes DF which will hold aggregated csv files

for f in glob.glob("*.csv"): #for all csv files in pwd
    df = pd.read_csv(f) #create dataframe for reading current csv
    all_data = all_data.append(df) #appends current csv to final DF

all_data.to_csv("final.csv", index=None)

Answer 1

I think there are more problems:

1. I removed import csv and import numpy as np , because in this demo they are not used (but maybe they in missing, lines so they can be imported)
2. I created list of all dataframes dfs , where dataframes are appended by dfs.append(df) . Then I used function concat for joining this list to final dataframe.
3. In function read_csv I added parameter header=None , because the main problem was that read_csv reads first row as header .
4. In function to_csv I added parameter header=None for omitting header.
5. I added folder test to final destination file, because if use function glob.glob("*.csv") you should read output file as input file.

Solution:

import glob
import pandas as pd

all_data = pd.DataFrame() #initializes DF which will hold aggregated csv files

#list of all df
dfs = []
for f in glob.glob("*.csv"): #for all csv files in pwd
    #add parameters to read_csv
    df = pd.read_csv(f, header=None) #create dataframe for reading current csv
    #print df
    dfs.append(df) #appends current csv to final DF
all_data = pd.concat(dfs, ignore_index=True)
print all_data
#       0      1      2
#0  test1  test1  test1
#1  test1  test1  test1
#2  test1  test1  test1
#3  test2  test2  test2
#4  test2  test2  test2
#5  test2  test2  test2
all_data.to_csv("test/final.csv", index=None, header=None)

Next solution is similar.
I add parameter header=None to read_csv and to_csv and add parameter ignore_index=True to append .

import glob
import pandas as pd

all_data = pd.DataFrame() #initializes DF which will hold aggregated csv files

for f in glob.glob("*.csv"): #for all csv files in pwd
    df = pd.read_csv(f, header=None) #create dataframe for reading current csv
    all_data = all_data.append(df, ignore_index=True) #appends current csv to final DF
print all_data
#       0      1      2
#0  test1  test1  test1
#1  test1  test1  test1
#2  test1  test1  test1
#3  test2  test2  test2
#4  test2  test2  test2
#5  test2  test2  test2

all_data.to_csv("test/final.csv", index=None, header=None)

Answer 2

You can concat . Let df1 be your first dataframe and df2 the second, you can:

df = pd.concat([df1,df2],ignore_index=True)

The ignore_index is optional, you can set it to True if you don't mind the original indexes of the single dataframes.

Answer 3

pandas is not a tool to use when all you want is to create a single csv file, you can simply write each csv to a new file as you go:

import glob

with open("out.csv","w") as out:
    for fle in glob.glob("*.csv"):
        with open(fle) as f:
             out.writelines(f)

Or with the csv lib if you prefer:

import glob
import csv

with open("out.csv", "w") as out:
    wr = csv.writer(out)
    for fle in glob.glob("*.csv"):
        with open(fle) as f:
            wr.writerows(csv.reader(f))  

Creating a large dataframe just to eventually write to disk makes no real sense, furthermore if you had a lot of large files it may not even be possible.