I use Stata 12.
I want to add some country code identifiers from file df_all_cities.csv onto my working data.
However, this line of code:
merge 1:1 city country using "df_all_cities.csv", nogen keep(1 3)
Gives me the error:
. run "/var/folders/jg/k6r503pd64bf15kcf394w5mr0000gn/T//SD44694.000000"
file df_all_cities.csv not Stata format
r(610);
This is an attempted solution to my previous problem of the file being a dta file not working on this version of Stata, so I used R to convert it to .csv, but that also doesn't work. I assume it's because the command itself "using" doesn't work with csv files, but how would I write it instead?
Your intuition is right. The command merge cannot read a .csv file directly. (using is technically not a command here, it is a common syntax tag indicating a file path follows.)
You need to read the .csv file with the command insheet. You can use it like this.
* Preserve saves a snapshot of your data which is brought back at "restore"
preserve
* Read the csv file. clear can safely be used as data is preserved
insheet using "df_all_cities.csv", clear
* Create a tempfile where the data can be saved in .dta format
tempfile country_codes
save `country_codes'
* Bring back into working memory the snapshot saved at "preserve"
restore
* Merge your country codes from the tempfile to the data now back in working memory
merge 1:1 city country using `country_codes', nogen keep(1 3)
See how insheet is also using using and this command accepts .csv files.
Stata version: 12.1
I get an error "file not found" using this code:
cd "$path_in"
insheet using "df_mcd_clean.csv", comma clear
append using "df_mcd15_clean.csv" #where error happens
append using "df_ingram_liu1998_clean.csv"
append using "df_wccd_clean.csv"
I double checked that the file is indeed called that and located in the directory.
append is for appending .dta files. Therefore, if you ask to append foo.csv Stata assumes you are referring to foo.csv.dta, which it can't find.
The solutions include
Combine the .csv files outside Stata.
Read in each .csv file, save as .dta, then append.
The current version of the help for append says this:
append appends Stata-format datasets stored on disk to the end of the dataset in memory. If any filename is
specified without an extension, .dta is assumed.
and that was true too in Stata 12. (Whether the wording was identical, you can say.)
Suppose that df is a dataframe in Spark. The way to write df into a single CSV file is
df.coalesce(1).write.option("header", "true").csv("name.csv")
This will write the dataframe into a CSV file contained in a folder called name.csv but the actual CSV file will be called something like part-00000-af091215-57c0-45c4-a521-cd7d9afb5e54.csv.
I would like to know if it is possible to avoid the folder name.csv and to have the actual CSV file called name.csv and not part-00000-af091215-57c0-45c4-a521-cd7d9afb5e54.csv. The reason is that I need to write several CSV files which later on I will read together in Python, but my Python code makes use of the actual CSV names and also needs to have all the single CSV files in a folder (and not a folder of folders).
Any help is appreciated.
A possible solution could be convert the Spark dataframe to a pandas dataframe and save it as csv:
df.toPandas().to_csv("<path>/<filename>")
EDIT: As caujka or snark suggest, this works for small dataframes that fits into driver. It works for real cases that you want to save aggregated data or a sample of the dataframe. Don't use this method for big datasets.
If you want to use only the python standard library this is an easy function that will write to a single file. You don't have to mess with tempfiles or going through another dir.
import csv
def spark_to_csv(df, file_path):
""" Converts spark dataframe to CSV file """
with open(file_path, "w") as f:
writer = csv.DictWriter(f, fieldnames=df.columns)
writer.writerow(dict(zip(fieldnames, fieldnames)))
for row in df.toLocalIterator():
writer.writerow(row.asDict())
If the result size is comparable to spark driver node's free memory, you may have problems with converting the dataframe to pandas.
I would tell spark to save to some temporary location, and then copy the individual csv files into desired folder. Something like this:
import os
import shutil
TEMPORARY_TARGET="big/storage/name"
DESIRED_TARGET="/export/report.csv"
df.coalesce(1).write.option("header", "true").csv(TEMPORARY_TARGET)
part_filename = next(entry for entry in os.listdir(TEMPORARY_TARGET) if entry.startswith('part-'))
temporary_csv = os.path.join(TEMPORARY_TARGET, part_filename)
shutil.copyfile(temporary_csv, DESIRED_TARGET)
If you work with databricks, spark operates with files like dbfs:/mnt/..., and to use python's file operations on them, you need to change the path into /dbfs/mnt/... or (more native to databricks) replace shutil.copyfile with dbutils.fs.cp.
A more databricks'y' solution is here:
TEMPORARY_TARGET="dbfs:/my_folder/filename"
DESIRED_TARGET="dbfs:/my_folder/filename.csv"
spark_df.coalesce(1).write.option("header", "true").csv(TEMPORARY_TARGET)
temporary_csv = os.path.join(TEMPORARY_TARGET, dbutils.fs.ls(TEMPORARY_TARGET)[3][1])
dbutils.fs.cp(temporary_csv, DESIRED_TARGET)
Note if you are working from Koalas data frame you can replace spark_df with koalas_df.to_spark()
For pyspark, you can convert to pandas dataframe and then save it.
df.toPandas().to_csv("<path>/<filename.csv>", header=True, index=False)
There is no dataframe spark API which writes/creates a single file instead of directory as a result of write operation.
Below both options will create one single file inside directory along with standard files (_SUCCESS , _committed , _started).
1. df.coalesce(1).write.mode("overwrite").format("com.databricks.spark.csv").option("header",
"true").csv("PATH/FOLDER_NAME/x.csv")
2. df.repartition(1).write.mode("overwrite").format("com.databricks.spark.csv").option("header",
"true").csv("PATH/FOLDER_NAME/x.csv")
If you don't use coalesce(1) or repartition(1) and take advantage of sparks parallelism for writing files then it will create multiple data files inside directory.
You need to write function in driver which will combine all data file parts to single file(cat part-00000* singlefilename ) once write operation is done.
I had the same problem and used python's NamedTemporaryFile library to solve this.
from tempfile import NamedTemporaryFile
s3 = boto3.resource('s3')
with NamedTemporaryFile() as tmp:
df.coalesce(1).write.format('csv').options(header=True).save(tmp.name)
s3.meta.client.upload_file(tmp.name, S3_BUCKET, S3_FOLDER + 'name.csv')
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-uploading-files.html for more info on upload_file()
Create temp folder inside output folder. Copy file part-00000* with the file name to output folder. Delete the temp folder. Python code snippet to do the same in Databricks.
fpath=output+'/'+'temp'
def file_exists(path):
try:
dbutils.fs.ls(path)
return True
except Exception as e:
if 'java.io.FileNotFoundException' in str(e):
return False
else:
raise
if file_exists(fpath):
dbutils.fs.rm(fpath)
df.coalesce(1).write.option("header", "true").csv(fpath)
else:
df.coalesce(1).write.option("header", "true").csv(fpath)
fname=([x.name for x in dbutils.fs.ls(fpath) if x.name.startswith('part-00000')])
dbutils.fs.cp(fpath+"/"+fname[0], output+"/"+"name.csv")
dbutils.fs.rm(fpath, True)
You can go with pyarrow, as it provides file pointer for hdfs file system. You can write your content to file pointer as a usual file writing. Code example:
import pyarrow.fs as fs
HDFS_HOST: str = 'hdfs://<your_hdfs_name_service>'
FILENAME_PATH: str = '/user/your/hdfs/file/path/<file_name>'
hadoop_file_system = fs.HadoopFileSystem(host=HDFS_HOST)
with hadoop_file_system.open_output_stream(path=FILENAME_PATH) as f:
f.write("Hello from pyarrow!".encode())
This will create a single file with the specified name.
To initiate pyarrow you should define environment CLASSPATH properly, set the output of hadoop classpath --glob to it
df.write.mode("overwrite").format("com.databricks.spark.csv").option("header", "true").csv("PATH/FOLDER_NAME/x.csv")
you can use this and if you don't want to give the name of CSV everytime you can write UDF or create an array of the CSV file name and give it to this it will work
I have several thousand .csv files in a folder and am trying to use the cdm to append them. Each file is the same table with top header and bottom notes. For example,
121030_2003.csv
121030_2004.csv
...
121031_2003.csv
121031_2004.csv
...
I tried copy *.csv all.csv from cmd and I would like to add code for the resulting file to have:
the header reported only once at the beginning, and possibly no notes
an additional column, reporting the name of the source file to keep track of it.
I think you can use cat in Linux.
e.g. for appending 1.txt 2.txt 3.txt to an output file 0.txt
cat 1.txt 2.txt 3.txt > 0.txt
csv files are just like txt files so it will be the same in your case, except that 'type' is used in cmd for 'cat'.
Actually my intention is to rename the output of a hadoop job to .csv files, because i need to visualize this csv data in rapidminer.
In How can i output hadoop result in csv format it is said, that for this purpose I need to follow these three steps:
1. Submit the MapReduce Job
2. Which will extract the output from HDFS using shell commands
3. Merge them together, rename as ".csv" and place in a directory where the visualization tool can access the final file
If so, how can I achieve this?
UPDATE
myjob.sh:
bin/hadoop jar /var/root/ALA/ala_jar/clsperformance.jar ala.clsperf.ClsPerf /user/root/ala_xmlrpt/Amrita\ Vidyalayam\,\ Karwar_Class\ 1\ B_ENG.xml /user/root/ala_xmlrpt-outputshell4
bin/hadoop fs -get /user/root/ala_xmlrpt-outputshell4/part-r-00000 /Users/jobsubmit
cat /Users/jobsubmit/part-r-00000 /Users/jobsubmit/output.csv
showing:
The CSV file was empty and couldn’t be imported.
when I tried to open output.csv.
solution
cat /Users/jobsubmit/part-r-00000> /Users/jobsubmit/output.csv
Firstly you need to retrieve MapReduce result from HDFS
hadoop dfs -copyToLocal path_to_result/part-r-* local_path
Then cat them into a single file
cat local_path/part-r-* > result.csv
Then it depends your MapReduce result format, if it's already a csv format, then it is done. If not, probably you have to use other tool like sed or awk to transform it into csv format.