Pandas 如何在循环期间通过在列上连接列来合并多个csv文件

这是我的问题。我有100个文件，它们都有两列：“时间坡度”和“坡度”。我想创建一个包含所有内容的文件。以下是一个例子：

-----file 1----
2001.1     10
2001.2     20
2001.3     12
2001.4      4
2001.5      1
2001.6     13

-----file 2----
2001.3     20
2001.4     15
2001.5     6

-----file 3----
2001.6     15
2001.7     15
2001.8     15
2001.9     20
2002.0     23

**The expected result is:**
------- output file ---------
date    file1 file2 file3
2001.1    10   NAN  NAN
2001.2    20   NAN  NAN         
2001.3    12   NAN  NAN          
2001.4     4    15  NAN                     
2001.5     1     6  NAN                     
2001.6    13   NAN   15
2001.7   NAN   NAN   15
2001.8   NAN   NAN   15
2001.9   NAN   NAN   20
2002.0   NAN   NAN   23

以下是我尝试过的：

import pandas as pd
import os, glob
import numpy as np

filename_list = []

file_path = r"C:\Users\Path"
for file in glob.glob(path + "/*.csv"):
    filename_list.append(file)

from numpy import genfromtxt
df_ini = pd.read_csv('output.csv')         #IN FILE OUTPUT THERE ARE ALREADY TWO COLUMNS WITH VALUES
df_ini.columns=['time_slopes','slope']      
for filename in filename_list:
    with open(filename, 'r') as f:
    # convert numpy array into DataFrame
    numpyarray = genfromtxt(f, delimiter=',')
    df = pd.DataFrame({'time_slopes':numpyarray[:, 0],'slope':numpyarray[:, 1]})
    # remove NaN values:
    df = df.dropna(how='all')
    # re-index file:
    df.reset_index(drop=True, inplace=True)
    # merge file:
    dfmerge = df_ini.merge(df,on='time_slopes',how='left')
    dfmerge.to_csv("output.csv", sep=',', index=False)

这段代码只返回两列——第一列（来自df_ini）和最后一列（来自文件号100）……在每次迭代中，最后一列被重写而不是添加。 日期文件1文件100 2001.1 10南

有人知道怎么解决这个问题吗？ 谢谢

这可能会对你有所帮助

file_1 = pd.DataFrame({'date': [2001.1, 2001.2, 2001.3], 'slope': [10, 20, 12]})
file_2 = pd.DataFrame({'date': [2001.4, 2001.5, 2001.6], 'slope': [20, 15, 6]})
file_3 = pd.DataFrame({'date': [2001.6, 2001.7, 2001.8], 'slope': [30, 40, 90]})

df_list = [file_1, file_2, file_3]
for df in df_list:
    df.index = df['date']
    df.drop(['date'], axis=1, inplace=True)

final_df = pd.concat(df_list, axis=1, ignore_index=True)
final_df = final_df.reset_index()

print(final_df)

输出：

     date     0     1     2
0  2001.1  10.0   NaN   NaN
1  2001.2  20.0   NaN   NaN
2  2001.3  12.0   NaN   NaN
3  2001.4   NaN  20.0   NaN
4  2001.5   NaN  15.0   NaN
5  2001.6   NaN   6.0  30.0
6  2001.7   NaN   NaN  40.0
7  2001.8   NaN   NaN  90.0