[英]List comprehension to iterate through dataframe
I have written code to encode one row of a dataframe to json, as follows:我已经编写代码将 dataframe 的一行编码为 json,如下所示:
def encode_df_metadata_row(df):
return {'name': df['Title'].values[0], 'code': df['Code'].values[0], 'frequency': df['Frequency'].values[0], 'description': df['Subtitle'].values[0], 'source': df['Source'].values[0]}
Now I would like to encode an entire dataframe to json with some transformation, so I wrote this function:现在我想通过一些转换将整个 dataframe 编码为 json,所以我写了这个 function:
def encode_metadata_list(df_metadata):
return [encode_df_metadata_row(df_row) for index, df_row in df_metadata.iterrows()]
I then try to call the function using this code:然后我尝试使用以下代码调用 function:
df_oodler_metadata = pd.read_csv('DATA\oodler-datasets-metadata.csv')
response = encode_metadata_list(df_oodler_metadata)
print(response)
When I run this code, I get the following error:当我运行此代码时,出现以下错误:
AttributeError: 'str' object has no attribute 'values'
I've tried a bunch of variations but I keep getting similar errors.我尝试了很多变体,但我不断收到类似的错误。 Does someone know the right way to do this?
有人知道这样做的正确方法吗?
DataFrame.iterrows
yields pairs of index
and row
, where each row
is a Series object. It stores a single element for each column, so the .values[0]
part in your encode_df_metadata_row(df)
function becomes irrelevant - the correct form of this function should be: DataFrame.iterrows
产生成对的index
和row
,其中每一row
都是一个系列object。它为每一列存储一个元素,因此encode_df_metadata_row(df)
function 中的.values[0]
部分变得无关紧要 - 这个的正确形式function 应该是:
def encode_df_metadata_row(row):
return {'name': row['Title'], 'code': row['Code'], 'frequency': row['Frequency'], 'description': row['Subtitle'], 'source': row['Source']}
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