Python 中 Pandas DataFrame 的 JSON 字典
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JSON dictionaries to Pandas DataFrame in Python
提问by MauricioRoman
I get JSON data from an API service, and I would like to use a DataFrame to then output the data into CSV.
我从 API 服务获取 JSON 数据，然后我想使用 DataFrame 将数据输出到 CSV 中。
So, I am trying to convert a list of dictionaries, with about 100.000 dictionaries with about 100 key value pairs, nested up to 4 levels deep, into a Pandas DataFrame.
因此，我正在尝试将包含大约 100.000 个字典和大约 100 个键值对的字典列表转换为 Pandas DataFrame，最多嵌套 4 层。
I am using the following code, but it is painfully slow:
我正在使用以下代码，但速度非常慢：
try:
# Convert each JSON data event to a Pandas DataFrame
df_i = []
for d in data:
df_i.append( json_normalize(d) )
# Concatenate all DataFrames into a single one
df = concat(df_i, axis=0)
except AttributeError:
print "Error: Expected a list of dictionaries to parse JSON data"
Does anyone know of a better and faster way to do this?
有谁知道更好更快的方法来做到这一点？
采纳答案by Andy Hayden
There's a whole section in the io docson reading json (as strings or files) directly using pd.read_json.
io 文档中有一个关于直接使用 .json 读取 json（作为字符串或文件）的完整部分pd.read_json。
You ought to be able to do something like:
您应该能够执行以下操作：
pd.concat((pd.read_json(d) for d in data), axis=0)
This will often be much faster than creating a temporary dict.
这通常比创建临时字典要快得多。
