“ValueError: cannot set a frame with no defined columns” 在追加两个数据框时出现。

huangapple
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英文:

While appending 2 dataframes getting "ValueError: cannot set a frame with no defined columns"

问题

数据帧1位于一个函数内，数据帧2位于主函数中，当我运行主函数时，它从JSON中附加值并存储在result_df中，出现ValueError: cannot set a frame with no defined columns错误。

为什么我要在主函数中创建数据帧2？
我在其他函数中使用数据帧2（total_device_df）将其转换为CSV。

可重现的代码：

  1. import pandas as pd
  2. import os
  3. import json
  5. class KatsRequest:
  6.     currDir = os.getcwd()
  7.     
  8.     def parse_json_response():
  9.         filename = "my_json_file.json"
  10.         device_name = ["Trona", "Sheldon"]
  11.         "创建数据帧以存储结果"
  12.         column_names = ["DEVICE", "STATUS", "LAST UPDATED"]
  13.         result_df = pd.DataFrame(columns=column_names)
  14.         my_json_file = currDir + '/' + filename
  16.         for i in range(len(device_name)):
  17.             my_device_name = device_name[i]
  18.             with open(my_json_file) as f:
  19.                 data = json.load(f)
  21.             for devices in data:
  22.                 device_types = devices['device_types']
  23.                 if my_device_name in device_types['name']:
  24.                     if device_types['name'] == my_device_name:
  25.                         device = devices['device_types']['name']
  26.                         last_updated = devices['devices']['last_status_update']
  27.                         device_status = devices['devices']['status']
  29.                         result_df.loc[len(result_df)] = {'DEVICE': device, 'STATUS': device_status, 'LAST UPDATED': last_updated}
  30.         return result_df
  32. def main():
  33.     total_device_df = pd.DataFrame()
  34.     total_device_df.loc[len(total_device_df)] = KatsRequest().parse_json_response()
  36. if __name__ == '__main__':
  37.     main()

这是我的JSON文件内容：（保存在当前路径中，命名为"my_json_file.json"）

  1. [{"devices": {"id": 34815, "last_status_update": "2023-05-25 07:56:49", "status": "idle" }, "device_types": {"name": "Trona"}}, {"devices": {"id": 34815, "last_status_update": "2023-05-25 07:56:49", "status": "idle" }, "device_types": {"name": "Sheldon"}}]

输出：
ValueError: cannot set a frame with no defined columns

这里缺少/错误了什么？

英文:

Dataframe 1 is inside a function and Dataframe 2 is in main function when I run main function, it appends the values from a JSON and stores in result_df and getting ValueError: cannot set a frame with no defined columns error

Why I am creating Dataframe2 in main function?
I am using Dataframe2 (total_device_df) in other functions to convert to csv.

Reproducible code:

  1. import pandas as pd
  2. import os
  3. import json
  5. class KatsRequest:
  7. currDir = os.getcwd()
  8.     def parse_json_response():
  10.         filename = "my_json_file.json"
  11.         device_name = ["Trona", "Sheldon"]
  12.         "creating dataframe to store result"
  13.         column_names = ["DEVICE", "STATUS", "LAST UPDATED"]
  14.         result_df = pd.DataFrame(columns=column_names)
  15.         my_json_file = currDir + '/' + filename
  17.         for i in range(len(device_name)):
  18.             my_device_name = device_name[i]
  19.             with open(my_json_file) as f:
  20.                 data = json.load(f)
  22.             for devices in data:
  23.                 device_types = devices['device_types']
  24.                 if my_device_name in device_types['name']:
  25.                     if device_types['name'] == my_device_name:
  26.                         device = devices['device_types']['name']
  27.                         last_updated = devices['devices']['last_status_update']
  28.                         device_status = devices['devices']['status']
  30.                         result_df.loc[len(result_df)] = {'DEVICE': device, 'STATUS': device_status, 'LAST UPDATED': last_updated}
  31.         return result_df
  33. def main()
  34.     total_device_df = pd.DataFrame()
  35.     total_device_df.loc[len(total_device_df)] = KatsRequest().parse_json_response(filename, device_names)
  37. if __name__ == '__main__':
  38.     main()

Here is my JSON file contents: (save in your current path named as "my_json_file.json")

  1. [{"devices": {"id": 34815, "last_status_update": "2023-05-25 07:56:49", "status": "idle" }, "device_types": {"name": "Trona"}}, {"devices": {"id": 34815, "last_status_update": "2023-05-25 07:56:49", "status": "idle" }, "device_types": {"name": "Sheldon"}}]

Output:
ValueError: cannot set a frame with no defined columns

What is missing/wrong here?

答案1

得分: 1

(你的示例无法重现)

如果你想要合并 total_device_df 和 parse_json_response 返回的数据框，你需要使用 pd.concat。

  1. def main():
  2.     total_device_df = pd.DataFrame()
  3.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])

或者简化为：

  1. def main():
  2.     total_device_df = KatsRequest().parse_json_response(filename, device_names)

完整示例：

  1. import os
  2. import json
  3. import pandas as pd
  5. currDir = os.getcwd()
  6. filename = 'my_json_file.json'
  7. device_names = ['Trona', 'Sheldon']
  9. class KatsRequest:
  10.     def parse_json_response(self, filename, device_name):
  12.         # 创建用于存储结果的数据框
  13.         column_names = ["DEVICE", "STATUS", "LAST UPDATED"]
  14.         result_df = pd.DataFrame(columns=column_names)
  15.         my_json_file = currDir + '/' + filename
  17.         for i in range(len(device_name)):
  18.             my_device_name = device_name[i]
  19.             with open(my_json_file) as f:
  20.                 data = json.load(f)
  22.             for devices in data:
  23.                 device_types = devices['device_types']
  24.                 if my_device_name in device_types['name']:
  25.                     if device_types['name'] == my_device_name:
  26.                         device = devices['device_types']['name']
  27.                         last_updated = devices['devices']['last_status_update']
  28.                         device_status = devices['devices']['status']
  30.                         result_df.loc[len(result_df)] = {'DEVICE': device, 'STATUS': device_status, 'LAST UPDATED': last_updated}
  31.         return result_df
  33. def main():
  34.     total_device_df = pd.DataFrame()
  35.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])
  36.     print(total_device_df)
  38. if __name__ == '__main__':
  39.     main()

输出：

  1. >>> total_device_df
  2.     DEVICE STATUS         LAST UPDATED
  3. 0    Trona   idle  2023-05-25 07:56:49
  4. 1  Sheldon   idle  2023-05-25 07:56:49

更新：

一种简便的方法是使用 pd.json_normalize：

  1. with open('my_json_file.json') as jp:
  2.     data = json.load(jp)
  3. total_device_df = (pd.json_normalize(data).loc[lambda x: x['device_types.name'].isin(device_names)]
  4.                      .rename(columns=lambda x: x.split('.', maxsplit=1)[-1].upper()))

输出：

  1. >>> total_device_df
  2.       ID   LAST_STATUS_UPDATE STATUS     NAME
  3. 0  34815  2023-05-25 07:56:49   idle    Trona
  4. 1  34815  2023-05-25 07:56:49   idle  Sheldon

如何在完整示例代码中使用 pd.json_normalize 方法？

  1. import os
  2. import json
  3. import pandas as pd
  5. currDir = os.getcwd()
  6. filename = 'my_json_file.json'
  7. device_names = ['Trona', 'Sheldon']
  9. class KatsRequest:
  10.     def parse_json_response(self, filename, device_name):
  12.         # 创建用于存储结果的数据框
  13.         dmap = {
  14.             'device_types.name': 'DEVICE',
  15.             'devices.status': 'STATUS',
  16.             'devices.last_status_update': 'LAST STATUS'
  17.         }
  18.         my_json_file = currDir + '/' + filename
  20.         with open(my_json_file) as f:
  21.             data = json.load(f)
  22.         results_df = (pd.json_normalize(data)[dmap.keys()].rename(columns=dmap)
  23.                         .loc[lambda x: x['DEVICE'].isin(device_names)])
  24.         return results_df
  26. def main():
  27.     total_device_df = pd.DataFrame()
  28.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])
  29.     print(total_device_df)
  31. if __name__ == '__main__':
  32.     main()

希望这些翻译对你有帮助。

英文:

(Your example is not reproducible)

You have to use pd.concat if you want to use merge total_device_df and the dataframe returned by parse_json_response.

  1. def main():
  2.     total_device_df = pd.DataFrame()
  3.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])

Or simply:

  1. def main():
  2.     total_device_df = pd.DataFrame()
  3.     total_device_df = KatsRequest().parse_json_response(filename, device_names)

Full example:

  1. import os
  2. import json
  3. import pandas as pd
  5. currDir = os.getcwd()
  6. filename = 'my_json_file.json'
  7. device_names = ['Trona', 'Sheldon']
  9. class KatsRequest:
  10.     def parse_json_response(self, filename, device_name):
  12.         # creating dataframe to store result
  13.         column_names = ["DEVICE", "STATUS", "LAST UPDATED"]
  14.         result_df = pd.DataFrame(columns=column_names)
  15.         my_json_file = currDir + '/' + filename
  17.         for i in range(len(device_name)):
  18.             my_device_name = device_name[i]
  19.             with open(my_json_file) as f:
  20.                 data = json.load(f)
  22.             for devices in data:
  23.                 device_types = devices['device_types']
  24.                 if my_device_name in device_types['name']:
  25.                     if device_types['name'] == my_device_name:
  26.                         device = devices['device_types']['name']
  27.                         last_updated = devices['devices']['last_status_update']
  28.                         device_status = devices['devices']['status']
  30.                         result_df.loc[len(result_df)] = {'DEVICE': device, 'STATUS': device_status, 'LAST UPDATED': last_updated}
  31.         return result_df
  33. def main():
  34.     total_device_df = pd.DataFrame()
  35.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])
  36.     print(total_device_df)
  38. if __name__ == '__main__':
  39.     main()

Output:

  1. >>> total_device_df
  2.     DEVICE STATUS         LAST UPDATED
  3. 0    Trona   idle  2023-05-25 07:56:49
  4. 1  Sheldon   idle  2023-05-25 07:56:49

Update:

A painless way is to use pd.json_normalize:

  1. with open('my_json_file.json') as jp:
  2.     data = json.load(jp)
  3. total_device_df = (pd.json_normalize(data).loc[lambda x: x['device_types.name'].isin(device_names)]
  4.                      .rename(columns=lambda x: x.split('.', maxsplit=1)[-1].upper()))

Output:

  1. >>> total_device_df
  2.       ID   LAST_STATUS_UPDATE STATUS     NAME
  3. 0  34815  2023-05-25 07:56:49   idle    Trona
  4. 1  34815  2023-05-25 07:56:49   idle  Sheldon

> How to use pd.json_normalize method in full example code?

  1. import os
  2. import json
  3. import pandas as pd
  5. currDir = os.getcwd()
  6. filename = 'my_json_file.json'
  7. device_names = ['Trona', 'Sheldon']
  9. class KatsRequest:
  10.     def parse_json_response(self, filename, device_name):
  12.         # creating dataframe to store result
  13.         dmap = {
  14.             'device_types.name': 'DEVICE',
  15.             'devices.status': 'STATUS',
  16.             'devices.last_status_update': 'LAST STATUS'
  17.         }
  18.         my_json_file = currDir + '/' + filename
  20.         with open(my_json_file) as f:
  21.             data = json.load(f)
  22.         results_df = (pd.json_normalize(data)[dmap.keys()].rename(columns=dmap)
  23.                         .loc[lambda x: x['DEVICE'].isin(device_names)])
  24.         return results_df
  26. def main():
  27.     total_device_df = pd.DataFrame()
  28.     total_device_df = pd.concat([total_device_df, KatsRequest().parse_json_response(filename, device_names)])
  29.     print(total_device_df)
  31. if __name__ == '__main__':
  32.     main()

huangapple
  • 本文由 发表于 2023年5月26日 14:11:03
  • 转载请务必保留本文链接：https://go.coder-hub.com/76338060.html
  • dataframe
  • pandas
  • python
  • python-3.x
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