如何使用python pandas连接csv文件组时删除重复的标题（多行）

Question

I'm concatenating a group (6-10 files) of .csv files into one .csv file using python pandas library. I want to remove header (rows) contains 7 lines for all csv files except first file. How do I do this?

import glob 
import pandas as pd 
#filenames = glob.glob(path + "/*.csv") 
filenames = glob.glob("*.csv") 
print(filenames) 
count_files = 0 dfs = [] 
for filename in filenames: 
  if count_files ==0: 
    dfs.append(pd.read_csv(filename)) 
    full_df =pd.concat(dfs) count_files += 1 
  else: 
    dfs.append(pd.read_csv(filename, sep=";", skiprows=[0]))    #dfs.append(pd.read_csv(filename)) 
  full_df =pd.concat(dfs) 
  count_files +=1 
full_df.to_csv( "combined_csv.csv",header = None, index=False, encoding='utf-8-sig')

Answer 1

#creating dummy csv's for your requirement.
## appending muliple csvs in to one single csv 

df=pd.DataFrame({'A':[1,1,1],
                 'B':[1,2,3],
                'C':[3,9,3],
                'D':[1,8,9]})

df1=pd.DataFrame({'A':[4,5,5],
                 'B':[1,1,2],
                'C':[2,2,8],
                'D':[6,4,3]})

df2=pd.DataFrame({'A':[9,1,1],
                 'B':[9,2,3],
                'C':[3,9,13],
                'D':[9,8,9]})

df3=pd.DataFrame({'A':[14,15,5],
                 'B':[1,11,2],
                'C':[12,12,8],
                'D':[6,4,3]})

df.to_csv("one.csv")
df1.to_csv("two.csv")
df2.to_csv("three.csv")
df3.to_csv("four.csv")

import os
csv_list = []
for root, dirs,files in os.walk(os.getcwd(), topdown=True):
    for name in files:
        csv_list.append(os.path.join(root, name))

print(csv_list)

['/home/vikas.rana/stck_flw/two.csv',
 '/home/vikas.rana/stck_flw/one.csv',
 '/home/vikas.rana/stck_flw/four.csv',
 '/home/vikas.rana/stck_flw/three.csv']

names = ['A','B','C','D']
combined_csv = pd.concat([pd.read_csv(f, header=None,skiprows=[0],names = names) for f in csv_list ],ignore_index=True)



print(combined_csv)
# output
        A   B   C   D
    0   4   1   2   6
    1   5   1   2   4
    2   5   2   8   3
    3   1   1   3   1
    4   1   2   9   8
    5   1   3   3   9
    6   14  1   12  6
    7   15  11  12  4
    8   5   2   8   3
    9   9   9   3   9
    10  1   2   9   8
    11  1   3   13  9

Answer 2

As everyone say, provinding some code would help to clarify your intention.

However, this could solve your problem. It consists on creating an auxiliary CSV file from the rest and then importing it to store it as a Pandas DataFrame (in case you need it).

Let's imagine FileName1.csv to have the following content:

ColumnName_1,ColumnName_2,ColumnName_3
data11,data12,data13
data21,data22,data33

And FileName2.csv to have the following content:

ColumnName_1,ColumnName_2,ColumnName_3
Row to be deleted
Row to be deleted
Row to be deleted
data2_11,data2_12,data2_13
data2_21,data2_22,data2_33

And let's suppose that you would like to retain the headers in file 1 and skip the first 4 rows of the second one.

import pandas as pd

# Define a function that gets the file content ignoring n first rows
def get_content(file_path,ignored_rows):
    f = open(file_path,'r')
    file_data = f.readlines()
    for line in file_data[ignored_rows:]:
        files_content.append(line.rstrip('\n'))

# Generate empty List to allocate files rows
files_content = []

# Read first file
get_content('Files/FileName1.csv',0)

# Read second file
get_content('Files/FileName2.csv',4)

# Generate Complete CSV File
with open('Files/FullData.csv','w') as f:
    for line in files_content:
        f.write(line+'\n')

df = pd.read_csv('Files/FullData.csv')

This is ready to read a small amount of files. If you need to read a number of files, you add another loop to apply the same code.