Python 将字典转换为 dataframe 并将其导出到 csv 文件

Question

I am trying to convert a dictionary to a dataframe and then exporting it to a csv file but for some reason when the program exports the dataframe it changes the columns and rows.

df = pd.read_csv('test.csv')
for i in range(len(df['name'])):
    names.append(df['name'][i])
    balances.append(df['balance'][i])





def acc_creation(name):
    names.append(name)
    dict = {'name': names, 'balance': balances}
    new_df = pd.DataFrame.from_dict(
        dict, orient='index', columns=['name', 'balance'])
    new_df.to_csv('test.csv', encoding='utf-8', sep=',',
                  header=False, na_rep=0, index=True)

then it stops working and this is the error i get:

Traceback (most recent call last):
  File "/Users/darkmbs/Desktop/python/test.py", line 9, in <module>
    balances.append(df['balance'][i])
  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/frame.py", line 3024, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/indexes/base.py", line 3082, in get_loc
    raise KeyError(key) from err
KeyError: 'balance'

the output I want:

name,balance
xyzz,0 
abc,0

the output I get:

name,xyzz,abcd   
balance,0,0

Answer 1

The issue you are facing is names and balances have different lengths. When you call acc_creation(name) you add an extra element to names ( names.append(name) ), but not to balance , so there is a length mismatch. Something you could do is:

import pandas as pd
import numpy as np

def acc_creation(name):
    names.append(name)
    balances.append(0) # Or any value
    new_df = pd.DataFrame(np.column_stack([names, balances]), 
                                   columns=['name', 'balance'])
    new_df.to_csv('test.csv', encoding='utf-8', sep=',',
                  header=True, na_rep=0, index=True)

Answer 2

I believe you just remove:

 orient='index',

If you are getting that error message, let us see some of the original csv so we can test it and understand why.