将多个 .txt 文件合并为 csv

*Python 新手。

我正在尝试将多个文本文件合并为 1 个 csv；下面的例子 -

文件名.csv

Alpha

0
0.1
0.15
0.2
0.25
0.3

文本1.txt

Alpha,Beta
0,10
0.2,20
0.3,30

文本2.txt

Alpha,Charlie
0.1,5
0.15,15

文本3.txt

Alpha,Delta
0.1,10
0.15,20
0.2,50
0.3,10

csv 文件中所需的输出：-

文件名.csv

Alpha  Beta  Charlie  Delta
  0     10     0        0
  0.1    0     5        10
  0.15   0     15       20
  0.2   20     0        50
  0.25   0     0        0
  0.3   30     0        10

我一直在使用的代码和提供的其他代码给了我类似于页面底部的答案

def mergeData(indir="Dir Path", outdir="Dir Path"):
    dfs = []
    os.chdir(indir)
    fileList=glob.glob("*.txt")
    for filename in fileList:
        left= "/Path/Final.csv"
        right = filename
        output = "/Path/finalMerged.csv"
        leftDf = pandas.read_csv(left)
        rightDf = pandas.read_csv(right)
        mergedDf = pandas.merge(leftDf,rightDf,how='inner',on="Alpha", sort=True)
        dfs.append(mergedDf)
    outputDf = pandas.concat(dfs, ignore_index=True)
    outputDf = pandas.merge(leftDf, outputDf, how='inner', on='Alpha', sort=True, copy=False).fillna(0)
    print (outputDf)

    outputDf.to_csv(output, index=0)

mergeData()

然而我得到的答案并不是期望的结果：-

Alpha  Beta  Charlie  Delta
  0     10     0        0
  0.1    0     5        0
  0.1    0     0        10
  0.15   0     15       0
  0.15   0     0        20
  0.2   20     0        0
  0.2    0     0        50
  0.25   0     0        0
  0.3   30     0        0
  0.3    0     0        10

IIUC 您可以创建所有列表DataFrames - dfs, 循环追加mergedDf最后concat http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html all DataFrames to one:

import pandas
import glob
import os

def mergeData(indir="dir/path", outdir="dir/path"):
    dfs = []
    os.chdir(indir)
    fileList=glob.glob("*.txt")
    for filename in fileList:
        left= "/path/filename.csv"
        right = filename
        output = "/path/filename.csv"
        leftDf = pandas.read_csv(left)
        rightDf = pandas.read_csv(right)
        mergedDf = pandas.merge(leftDf,rightDf,how='right',on="Alpha", sort=True)
        dfs.append(mergedDf)
    outputDf = pandas.concat(dfs, ignore_index=True)
    #add missing rows from leftDf (in sample Alpha - 0.25) 
    #fill NaN values by 0
    outputDf = pandas.merge(leftDf,outputDf,how='left',on="Alpha", sort=True).fillna(0)
    #columns are converted to int
    outputDf[['Beta', 'Charlie']] = outputDf[['Beta', 'Charlie']].astype(int) 
    print (outputDf)

    outputDf.to_csv(output, index=0)

mergeData()

   Alpha  Beta  Charlie
0   0.00    10        0
1   0.10     0        5
2   0.15     0       15
3   0.20    20        0
4   0.25     0        0
5   0.30    30        0

EDIT:

问题是你改变了参数how='left'在第二个merge to how='inner':

def mergeData(indir="Dir Path", outdir="Dir Path"):
    dfs = []
    os.chdir(indir)
    fileList=glob.glob("*.txt")
    for filename in fileList:
        left= "/Path/Final.csv"
        right = filename
        output = "/Path/finalMerged.csv"
        leftDf = pandas.read_csv(left)
        rightDf = pandas.read_csv(right)
        mergedDf = pandas.merge(leftDf,rightDf,how='inner',on="Alpha", sort=True)
        dfs.append(mergedDf)
    outputDf = pandas.concat(dfs, ignore_index=True)
    #need left join, not inner
    outputDf = pandas.merge(leftDf, outputDf, how='left', on='Alpha', sort=True, copy=False)
                     .fillna(0)
    print (outputDf)

    outputDf.to_csv(output, index=0)

mergeData()

   Alpha  Beta  Charlie  Delta
0   0.00  10.0      0.0    0.0
1   0.10   0.0      5.0    0.0
2   0.10   0.0      0.0   10.0
3   0.15   0.0     15.0    0.0
4   0.15   0.0      0.0   20.0
5   0.20  20.0      0.0    0.0
6   0.20   0.0      0.0   50.0
7   0.25   0.0      0.0    0.0
8   0.30  30.0      0.0    0.0
9   0.30   0.0      0.0   10.0