Python Pandas DataFrame iloc索引报错：非整数键问题修复

错误信息 #

/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis) 1620 key = item_from_zerodim(key) 1621 if not is_integer(key):
-> 1622 raise TypeError("Cannot index by location index with a non-integer key") 1623 1624 # validate the location

TypeError: Cannot index by location index with a non-integer key

错误出现在代码行 data.iloc[[x]["Date"]] == "1/9/"+str(year)+"12:00:00"，尝试多种方案无效，求修复方法。

相关代码 #

#Calculate the cost, revenue, and profit for each day in each year.
def calculateParametersOnedayEachMonth(ptype,month, year):

  price = 0
  r = 0
  
  data = pd.DataFrame(readData)
  #print(data[["Date"]])

  for x in range(data.size):
    #print(x)
    if data.iloc[[x]["Date"]] == "1/"+month+"/"+str(year)+"12:00:00" :
       print("I am inside")
       index1 = x
       index2 = x+24
       for index1 in range(index2):
         if type == "PV":
           price = price + data.iloc[[index1]['PVPrice']]
           print(price)
           loadForcast = data.iloc[[index1]['PVLoadForecast']]
           powerForcast = data.iloc[[index1]['PVPowerForecast']]
           r = r+(loadForcast-powerForcast)  
           print(r)
         elif type == "DG":
           price = price + data.iloc[[index1]['DGPrices']]
           loadForcast = data.iloc[[index1]['DGLoad forecast']]
           powerForcast = data.iloc[[index1]['DGPowerForecast']]
           r = r+(loadForcast-powerForcast)
         elif type == "Wind":
           price = price + data.iloc[[index1]['WPrice']]
           loadForcast = data.iloc[[index1]['WindLoadforecast']]
           powerForcast = data.iloc[[index1]['WindPowerForecast']]
           r = r+(loadForcast-powerForcast)
         elif type == "BES":
           price = price + data.iloc[[index1]['BESPrice']]
           loadForcast = data.iloc[[index1]['BESLoadForecast']]
           powerForcast = data.iloc[[index1]['BESPowerForecast']]
           r = r+(loadForcast-powerForcast)
         elif type == "Zonal":
           price = price + data.iloc[[index1]['ZonalPrice']]
           loadForcast = data.iloc[[index1]['ZonalLoadForecast']]
           powerForcast = data.iloc[[index1][' ZonalPowerForecast']]
           r = r+(loadForcast-powerForcast)
         else: print("Sorry, this type is not provided by dataset")
       break
   

  avgPrice = price / 24
  avgRevenue = r/24

  print("Day-Ahead Energy Price",avgPrice)
  print("Day-Ahead Revenue",avgPrice)
 

  allValues = {  "Cost": avgPrice , "Reveneu": avgRevenue}

  if type=="PV":
    if year == "2011":
      listAllDaysParameters2011_PV.append(allValues)
    elif year == "2012":
      listAllDaysParameters2012_PV.append(allValues)
    elif year == "2013":
      listAllDaysParameters2013_PV.append(allValues)
    elif year == "2014":
      listAllDaysParameters2014_PV.append(allValues)
    elif year == "2015":
      listAllDaysParameters2015_PV.append(allValues)
    elif year == "2016":
      listAllDaysParameters2016_PV.append(allValues)
    else: print("This year is not provided by the dataset")

  elif type=="DG":
    if year == 2011:
      listAllDaysParameters2011_DG.append(allValues)
    elif year == 2012:
      listAllDaysParameters2012_DG.append(allValues)
    elif year == 2013:
      listAllDaysParameters2013_DG.append(allValues)
    elif year == 2014:
      listAllDaysParameters2014_DG.append(allValues)
    elif year == 2015:
      listAllDaysParameters2015_DG.append(allValues)
    elif year == 2016:
      listAllDaysParameters2016_DG.append(allValues)
    else: print("This year is not provided by the dataset")

  elif type=="Wind":
    if year == 2011:
      listAllDaysParameters2011_Wind.append(allValues)
    elif year == 2012:
      listAllDaysParameters2012_Wind.append(allValues)
    elif year == 2013:
      listAllDaysParameters2013_Wind.append(allValues)
    elif year == 2014:
      listAllDaysParameters2014_Wind.append(allValues)
    elif year == 2015:
      listAllDaysParameters2015_Wind.append(allValues)
    elif year == 2016:
      listAllDaysParameters2016_Wind.append(allValues)
    else: print("This year is not provided by the dataset")

  elif type=="BES":
    if year == 2011:
      listAllDaysParameters2011_BES.append(allValues)
    elif year == 2012:
      listAllDaysParameters2012_BES.append(allValues)
    elif year == 2013:
      listAllDaysParameters2013_BES.append(allValues)
    elif year == 2014:
      listAllDaysParameters2014_BES.append(allValues)
    elif year == 2015:
      listAllDaysParameters2015_BES.append(allValues)
    elif year == 2016:
      listAllDaysParameters2016_BES.append(allValues)
    else: print("This year is not provided by the dataset")

  elif type=="Zonal":
    if year == 2011:
      listAllDaysParameters2011_Zonal.append(allValues)
    elif year == 2012:
      listAllDaysParameters2012_Zonal.append(allValues)
    elif year == 2013:
      listAllDaysParameters2013_Zonal.append(allValues)
    elif year == 2014:
      listAllDaysParameters2014_Zonal.append(allValues)
    elif year == 2015:
      listAllDaysParameters2015_Zonal.append(allValues)
    elif year == 2016:
      listAllDaysParameters2016_Zonal.append(allValues)
    else: print("Sorry, this year is not provided by the dataset")

  else: print("Sorry, this type is not provided by the dataset")

1. 修正iloc的核心语法错误 #

你写的 data.iloc[[x]["Date"]] 完全不符合Pandas索引规则：

• iloc 是位置索引，只能接受整数、整数列表或切片，不能直接用列名
• [x]["Date"] 语法错误，x是整数，无法从整数中取"Date"键

正确写法是先按位置取行，再取列：

data.iloc[x]["Date"]
# 更高效的写法：先取列再按位置索引
data["Date"].iloc[x]

同时日期字符串缺少空格，修正后的错误行应为：

if data.iloc[x]["Date"] == f"1/{month}/{year} 12:00:00":

2. 批量修正所有iloc错误用法 #

代码中所有类似 data.iloc[[index1]['PVPrice']] 的写法都要调整，比如：

# 错误写法
price = price + data.iloc[[index1]['PVPrice']]
# 正确写法
price = price + data.iloc[index1]['PVPrice']
# 或更高效的写法
price = price + data['PVPrice'].iloc[index1]

3. 修复变量名冲突 #

函数参数是ptype，但代码里全用了type，这会覆盖Python内置的type()函数，导致逻辑错误。所有if type == "PV"这类语句都要改成if ptype == "PV"。

4. 细节修正 #

• 打印avgRevenue时误写为avgPrice，修正为：print("Day-Ahead Revenue", avgRevenue)
• 字典键拼写错误："Reveneu"改为"Revenue"
• 注意列名空格问题，比如" ZonalPowerForecast"要和实际数据列名完全一致，避免取值失败

5. 可选：优化循环逻辑（更符合Pandas风格） #

用for x in range(data.size)遍历行效率极低，建议用Pandas向量化操作替代：

# 先把Date列转为datetime类型，避免字符串匹配问题
data['Date'] = pd.to_datetime(data['Date'])
# 构造目标日期
target_date = pd.to_datetime(f"{year}-{month}-01 12:00:00")
# 找到匹配行的索引
match_row = data[data['Date'] == target_date].index[0]
# 直接取后续24行数据
subset = data.iloc[match_row:match_row+24]

这样可以完全替代外层for循环，代码更简洁高效。

内容的提问来源于stack exchange，提问作者Esra