将多个 .txt 文件合并到 csv 中

Question

*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

text1.txt

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

text2.txt

Alpha,Charlie
0.1,5
0.15,15

text3.txt

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

csv 文件中的所需输出：-

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一个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，我得到的答案不是预期的结果：-

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

Answer 1

IIUC 你可以创建所有DataFrames - dfs 的列表，在循环中追加mergedDf 和最后一个concat 所有DataFrames 到一个：

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

编辑：

问题是您将第二个merge 中的参数how='left' 更改为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

Answer 2

import pandas as pd
data1 = pd.read_csv('samp1.csv',sep=',')
data2 = pd.read_csv('samp2.csv',sep=',')
data3 = pd.read_csv('samp3.csv',sep=',')
df1 = pd.DataFrame({'Alpha':data1.Alpha})
df2 = pd.DataFrame({'Alpha':data2.Alpha,'Beta':data2.Beta})
df3 = pd.DataFrame({'Alpha':data3.Alpha,'Charlie':data3.Charlie})
mergedDf = pd.merge(df1, df2, how='outer', on ='Alpha',sort=False)
mergedDf1 = pd.merge(mergedDf, df3, how='outer', on ='Alpha',sort=False)
a = pd.DataFrame(mergedDf1)
print(a.drop_duplicates())

output:
  Alpha  Beta  Charlie
0   0.00  10.0      NaN
1   0.10   NaN      5.0
2   0.15   NaN     15.0
3   0.20  20.0      NaN
4   0.25   NaN      NaN
5   0.30  30.0      NaN