问题
I have been working on Spark Cluster using Data Proc google cloud services for Machine Learning Modelling. I have been successful to load the data from the Google Storage bucket. However, I am not sure how to write the panda's data frame and spark data frame to the cloud storage bucket as csv.
When I use the below command it gives me an error
df.to_csv("gs://mybucket/")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py", line 1745, in to_csv
formatter.save()
File "/opt/conda/lib/python3.6/site-packages/pandas/io/formats/csvs.py", line 156, in save
compression=self.compression)
File "/opt/conda/lib/python3.6/site-packages/pandas/io/common.py", line 400, in _get_handle
f = open(path_or_buf, mode, encoding=encoding)
FileNotFoundError: [Errno 2] No such file or directory: 'gs://dataproc-78f5e64b-a26d-4fe4-bcf9-e1b894db9d8f-au-southeast1/trademe_xmas.csv'
FileNotFoundError: [Errno 2] No such file or directory: 'gs://mybucket/'
however the following command work but I am not sure where it is saving the file
df.to_csv("data.csv")
I also followed the below article and it gives the following error Write a Pandas DataFrame to Google Cloud Storage or BigQuery
import google.datalab.storage as storage
ModuleNotFoundError: No module named 'google.datalab'
I am relatively new to Google Cloud Data Proc and Spark and I was hoping if someone can help me understand how can I save my output pandas data frame to gcloud bucket
Thanks in Advance !!
########For Igor as Requestedfrom pyspark.ml.classification import RandomForestClassifier as RF
rf = RF(labelCol='label', featuresCol='features',numTrees=200)
fit = rf.fit(trainingData)
transformed = fit.transform(testData)
from pyspark.mllib.evaluation import BinaryClassificationMetrics as metric
results = transformed.select(['probability', 'label'])
#Decile Creation for the Output
test = results.toPandas()
test['X0'] = test.probability.str[0]
test['X1'] = test.probability.str[1]
test = test.drop(columns=['probability'])
test = test.sort_values(by='X1', ascending=False)
test['rownum'] = test.reset_index().index
x = round(test['rownum'].count() / 10)
test['rank'] = (test.rownum - 1)//x + 1
回答1:
The easiest should be to convert Pandas DataFrame to Spark DataFrame and write it to GCS.
Here's instructions on how to do this: https://stackoverflow.com/a/45495969/3227693
来源：https://stackoverflow.com/questions/53148394/upload-pandas-dataframe-to-gcp-bucket-for-dataproc