将谷歌存储桶中的所有 .csv 文件读取到一个大熊猫 df 中，然后以 .csv 格式保存到另一个桶中

Question

在我的谷歌云函数（Python 3.7 运行时）中，我创建了一个函数，它试图将所有 .csv 文件从谷歌存储桶下载到熊猫数据框 (df) 中。一旦进入数据帧，我将对其进行一些简单的 ETL 工作，然后将其转换回一个大的 .csv 文件以保存到另一个存储桶。
我遇到的问题是，当我将对象（使用 file.download_as_string() 转换为字符串）读入 read_csv() 时，我收到与 IO.StringIO 相关的错误（TypeError: initial_value must be str或无，不是字节）

在 read_csv(io.String.IO(file_contents)....) 中，这与我放置 io.StringIO 方法的位置有关吗？谁能帮我纠正这个错误？

    def stage1slemonthly(data, context, source_bucket = 'my_source_bucket', 
    destination_bucket = 'gs://my destination_bucket'):  


        from google.cloud import storage
        import pandas as pd
        import pyspark
        from pyspark.sql import SQLContext
        import io

        storage_client = storage.Client()

        # source_bucket = data['bucket']
        # source_file = data['name']
        source_bucket = storage_client.bucket(source_bucket)

        # load in the col names
        col_names = ["Customer_Country_Number", "Customer_Name", "Exclude",
             "SAP_Product_Name", "CP_Sku_Code", "Exclude", "UPC_Unit",
             "UPC_Case", "Colgate_Month_Year", "Total_Cases",
             "Promoted_Cases", "Non_Promoted_Cases",
             "Planned_Non_Promoted_Cases", "Exclude",
             "Lead_Measure", "Tons", "Pieces", "Liters",
             "Tons_Conv_Revenue", "Volume_POS_Units", "Scan_Volume",
             "WWhdrl_Volume", "Exclude", "Exclude", "Exclude", "Exclude",
             "Exclude", "Exclude", "Exclude", "Exclude", "Investment_Buy",
             "Exclude", "Exclude", "Gross_Sales", "Claim_Sales",
             "Adjusted_Gross_Sales", "Exclude", "Exclude",
             "Consumer_Investment", "Consumer_Allowance",
             "Special_Pack_FG", "Coupons", "Contests_Offers", 
             "Consumer_Price_Reduction", "Permanent_Price_Reduction",
             "Temporary_Price_Reduction", "TPR_Off_Invoice", "TPR_Scan",
             "TPR_WWdrwl_Exfact", "Every_Day_Low_Price", "Closeouts",
             "Inventory_Price_Reduction", "Exclude", "Customer_Investment",
             "Prompt_Payment", "Efficiency_Drivers", "Efficient_Logistics",
             "Efficient_Management", "Business_Builders_Direct", "Assortment",
             "Customer_Promotions","Customer_Promotions_Terms",
             "Customer_Promotions_Fixed", "Growth_Direct",
             "New_Product_Incentives", "Free_Goods_Direct",
             "Shopper_Marketing", "Business_Builders_Indirect",
             "Middleman_Performance", "Middleman_Infrastructure",
             "Growth_Indirect", "Indirect_Retailer_Investments",
             "Free_Goods_Indirect", "Other_Customer_Investments",
             "Product_Listing_Allowances", "Non_Performance_Trade_Payments",
             "Exclude", "Variable_Rebate_Adjustment", 
             "Overlapping_OI_Adjustment", "Fixed_Accruals",
             "Variable_Accruals", "Total_Accruals", "Gross_To_Net",
             "Invoiced_Sales", "Exclude", "Exclude", "Net_Sales",
             "Exclude", "Exclude", "Exclude", "Exclude", "Exclude",
             "Exclude", "Exclude", "Exclude", "Exclude",
             "Total_Variable_Cost", "Margin", "Exclude"]

        df = pd.DataFrame(columns=[col_names])

        for file in list(source_bucket.list_blobs()):
          file_contents = file.download_as_string() 
          df = df.append(pd.read_csv(io.StringIO(file_contents), header=None, names=[col_names]))

        df = df.reset_index(drop=True)

        # do ETL work here in future

        sc = pyspark.SparkContext.getOrCreate()
        sqlCtx = SQLContext(sc)
        sparkDf = sqlCtx.createDataFrame(df)
        sparkDf.coalesce(1).write.option("header", "true").csv(destination_bucket)

当我运行它时，我收到以下错误消息...

回溯（最近一次调用最后一次）：文件“/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py”，第 383 行，在 run_background_function _function_handler.invoke_user_function(event_object)文件“/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py”，第 217 行，invoke_user_function return call_user_function(request_or_event) 文件“/env/local/lib/python3.7 /site-packages/google/cloud/functions/worker.py”，第 214 行，在 call_user_function event_context.Context(**request_or_event.context)) 文件“/user_code/main.py”，第 56 行，在 stage1slemonthly df = df .append(pd.read_csv(io.StringIO(file_contents), header=None, names=[col_names])) TypeError: initial_value must be str or None, not bytes

Answer 1

您收到此错误是因为 file.download_as_string() return type 是 bytes 而不是 str，并且您不能将 io.StringIO 与 bytes 参数一起使用 (initial_value=file_contents)。

此外，col_names 在这里被定义为一个数组，所以写pd.DataFrame(columns=[col_names]) 和pd.read_csv(..., names=[col_names]) 是不正确的：你应该使用col_names 而不是[col_names]。

无论如何，从 Google Cloud Storage 读取 CSV 文件似乎不是正确的方法。你宁愿写：

from google.cloud import storage
import pandas as pd
import io

storage_client = storage.Client()

source_bucket = storage_client.bucket(source_bucket)

col_names = ["Customer_Country_Number", "Customer_Name", ...]

df = pd.DataFrame(columns=col_names)

for file in list(source_bucket.list_blobs()):
    file_path="gs://{}/{}".format(file.bucket.name, file.name)
    df = df.append(pd.read_csv(file_path, header=None, names=col_names))

# the rest of your code

确实，你可以read files directly from GCS 和pandas 的read_csv 方法，而不是下载文件来加载它，但你需要先安装gcsfs (pip3 install gcsfs)。