Reading all .csv files from a google storage bucket into one large pandas df, then saving back as .csv to another bucket

Question

Within my Google Cloud Function (Python 3.7 Runtime) I have created a function that is trying to download all .csv files from a google storage bucket into a pandas dataframe (df). Once in a dataframe, I was going to do some light ETL work to it then convert it back one large .csv file to save to another bucket.
The problem I'm getting is when I get to the point of reading in the objects (converted to strings using file.download_as_string()) into read_csv(), I get an error related to IO.StringIO (TypeError: initial_value must be str or None, not bytes)

Within the read_csv(io.String.IO(file_contents)....), Does this have something to do with where I have placed the io.StringIO method? Can anyone help me correct this error?

    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)

When I run it, I get the following error message...

Traceback (most recent call last): File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 383, in run_background_function _function_handler.invoke_user_function(event_object) File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 217, in invoke_user_function return call_user_function(request_or_event) File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 214, in call_user_function event_context.Context(**request_or_event.context)) File "/user_code/main.py", line 56, in 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

You get this error because file.download_as_string() return type is bytes and not str, and you cannot use io.StringIO with a bytes parameter (initial_value=file_contents).

Moreover, col_names is defined as an array here, so writing pd.DataFrame(columns=[col_names]) and pd.read_csv(..., names=[col_names]) is incorrect : you should use col_names instead of [col_names].

Anyway, it does not seem to be the right way to read a CSV file from Google Cloud Storage. You'd rather write :

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

Indeed, you can read files directly from GCS with read_csv method of pandas instead of downloading the file to load it, but you need to install gcsfs (pip3 install gcsfs) first.