Pandas DataFrame - KeyError：“日期”

Question

For a current project, I am working with a large Pandas DataFrame sourced from a JSON file.

As soon as calling specific objects of the JSON file within Pandas, I am getting key errors such as KeyError: 'date' for line df['date'] = pd.to_datetime(df['date']) .

I have already excluded the identifier/object wording as a possible source for the error. Is there any smart tweak to make this code work?

The JSON file has the following structure:

[
{"stock_symbol": "AMG", "date": "2013-01-01", "txt_main": "ABC"}
]

And the corresponding code section looks like this:

import string
import json
import pandas as pd

# Loading and normalising the input file
file = open("sp500.json", "r")
data = json.load(file)
df = pd.json_normalize(data)
df = pd.DataFrame().fillna("")

# Datetime conversion
df['date'] = pd.to_datetime(df['date'])

Answer 1

Take a look at the documentation examples of fillna function fillna function .

By doing df = pd.DataFrame().fillna("") you are overriding your previous df with a new (empty) dataframe. You can just apply it this way: df = df.fillna("") .

Answer 2

In [42]: import string
    ...: import json
    ...: import pandas as pd
    ...:
    ...: # Loading and normalising the input file
    ...: #file = open("sp500.json", "r")
    ...: #data = json.load(file)
    ...: df = pd.json_normalize(a)
    ...: #df = pd.DataFrame().fillna("")
    ...:
    ...: # Datetime conversion
    ...: df['date'] = pd.to_datetime(df['date'])

In [43]: df
Out[43]:
  stock_symbol       date txt_main
0          AMG 2013-01-01      ABC

df = pd.DataFrame().fillna("") creates a new empty dataframe and fills "NaN" with empty.

So, change that line to df = df.fillna("")

Answer 3

You are using df = pd.DataFrame().fillna("") which will create a new dataframe and fill an with no value.

Here the old df is replaced by empty dataframe, so there is no column named date . Instead, you can use to fill 'na' values using df.fillna("") .

import string
import json
import pandas as pd

# Loading and normalising the input file
file = open("sp500.json", "r")
data = json.load(file)
df = pd.json_normalize(data)
df = df.fillna("")

# Datetime conversion
df['date'] = pd.to_datetime(df['date'])

Thank you