I have a list of dataframes (df_cleaned) created from multiple csv files chosen by the user.
My objective is to save each dataframe within the df_cleaned list as a separate csv file locally.
I have the following code done which saves the file with its original title. But I see that it overwrites and manages to save a copy of only the last dataframe.
How can I fix it? According to my very basic knowledge perhaps I could use a break-continue statement in the loop? But I do not know how to implement it correctly.
for i in range(len(df_cleaned)):
outputFile = df_cleaned[i].to_csv(r'C:\...\Data Docs\TrainData\{}.csv'.format(name))
print('Saving of files as csv is complete.')
You can create a different name for each file, as an example in the following I attach the index to name:
for i in range(len(df_cleaned)):
outputFile = df_cleaned[i].to_csv(r'C:\...\Data Docs\TrainData\{0}_{1}.csv'.format(name,i))
print('Saving of files as csv is complete.')
this will create a list of files named <name>_N.csv with N = 0, ..., len(df_cleaned)-1.
A very easy way of solving. Just figured out the answer myself. Posting to help someone else.
fileNames is a list I created at the start of the code to save the
names of the files chosen by the user.
for i in range(len(df_cleaned)):
outputFile = df_cleaned[i].to_csv(r'C:\...\TrainData\{}.csv'.format(fileNames[i]))
print('Saving of files as csv is complete.')
Saves a separate copy for each file in the defined directory.
Related
I am trying to loop through multiple folders and subfolders in Azure Blob container and read multiple xml files.
Eg: I have files in YYYY/MM/DD/HH/123.xml format
Similarly I have multiple sub folders under month, date, hours and multiple XML files at last.
My intention is to loop through all these folder and read XML files. I have tried using few Pythonic approaches which did not give me the intended result. Can you please help me with any ideas in implementing this?
import glob, os
for filename in glob.iglob('2022/08/18/08/225.xml'):
if os.path.isfile(filename): #code does not enter the for loop
print(filename)
import os
dir = '2022/08/19/08/'
r = []
for root, dirs, files in os.walk(dir): #Code not moving past this for loop, no exception
for name in files:
filepath = root + os.sep + name
if filepath.endswith(".xml"):
r.append(os.path.join(root, name))
return r
The glob is a python function and it won't recognize the blob folders path directly as code is in pyspark. we have to give the path from root for this. Also, make sure to specify recursive=True in that.
For Example, I have checked above pyspark code in databricks.
and the OS code as well.
You can see I got the no result as above. Because for the above, we need to give the absolute root. it means the root folder.
glob code:
import glob, os
for file in glob.iglob('/path_from_root_to_folder/**/*.xml',recursive=True):
print(file)
For me in databricks the root to access is /dbfs and I have used csv files.
Using os:
You can see my blob files are listed from folders and subfolders.
I have used databricks for my repro after mounting. Wherever you are trying this code in pyspark, make sure you are giving the root of the folder in the path. when using glob, set the recursive = True as well.
There is an easier way to solve this problem with PySpark!
The tough part is all the files have to have the same format. In the Azure databrick's sample directory, there is a /cs100 folder that has a bunch of files that can be read in as text (line by line).
The trick is the option called "recursiveFileLookup". It will assume that the directories are created by spark. You can not mix and match files.
I added to the data frame the name of the input file for the dataframe. Last but not least, I converted the dataframe to a temporary view.
Looking at a simple aggregate query, we have 10 unique files. The biggest have a little more than 1 M records.
If you need to cherry pick files for a mixed directory, this method will not work.
However, I think that is an organizational cleanup task, versus easy reading one.
Last but not least, use the correct formatter to read XML.
spark.read.format("com.databricks.spark.xml")
I have a function in python, that basically merges three txt files into one file, in xlsx format.
For that I use pandas package.
So I am running the python function in a certain directory. This function has the input as a specific path. Then the function takes this path, list the files of the directory, and filter the files that are needed. Meaning that, since I only want to read the txt files, I then filter the txt files. However, when I try to convert this txt files into pandas dataframe, the dataframe is None.
Also, I want to write a final xlsx to the directory where the initial files are.
Here is my function:
def concat_files(path):
summary=''
files_separate=[]
arr2 = os.listdir(mypath)
for i, items_list in enumerate(arr2):
if len(items_list) > 50:
files_separate.append(items_list)
files_separate
chunks= [files_separate[x:x+3] for x in range(0,len(files_separate),3)]
while chunks:
focus=chunks.pop(0)
for items_1 in focus:
if items_1.endswith('.Cox1.fastq.fasta.usearch_cluster_fast.fasta.reps.fasta.blastn.report.txt.all_together.txt'):
pandas_dataframe=pd.Dataframe(example)
pandas_dataframe.to_excel('destiny_path/' + str(header_file)+'.final.xlsx')
you need to create the folders before exporting the xlsx files.
so assuming you already have the folders created.
change this line
pandas_dataframe.to_excel('destiny_path/' + str(header_file)+'.final.xlsx')
to
pandas_dataframe.to_excel(os.path.join('destiny_path' ,str(header_file),'.final.xlsx'))
I am attempting to move a couple thousand pdfs from one file location to another. The source folder contains multiple subfolders and I am combining just the pdfs (technical drawings) into one folder to simplify searching for the rest of my team.
The main goal is to only copy over files that do not already exist in the destination folder. I have tried a couple different options, most recently what is shown below, and in all cases, every file is copied every time. Prior to today, any time I attempted a bulk file move, I would received errors if the file existed in the destination folder but I no longer do.
I have verified that some of the files exist in both locations but are still being copied. Is there something I am missing or can modify to correct?
Thanks for the assistance.
import os.path
import shutil
source_folder = os.path.abspath(r'\\source\file\location')
dest_folder = os.path.abspath(r'\\dest\folder\location')
for folder, subfolders, files in os.walk(source_folder):
for file in files:
path_file=os.path.join(folder, file)
if os.path.exists(file) in os.walk(dest_folder):
print(file+" exists.")
if not os.path.exists(file) in os.walk(dest_folder):
print(file+' does not exist.')
shutil.copy2(path_file, dest_folder)
os.path.exists returns a Boolean value. os.walk creates a generator which produces triples of the form (dirpath, dirnames, filenames). So, that first conditional will never be true.
Also, even if that conditional were correct, your second conditional has a redundancy since it's merely the negation of the first. You could replace it with else.
What you want is something like
if file in os.listdir(dest_folder):
...
else:
...
I'm running a simple PySpark script, like this.
base_path = '/mnt/rawdata/'
file_names = ['2018/01/01/ABC1_20180101.gz',
'2018/01/02/ABC2_20180102.gz',
'2018/01/03/ABC3_20180103.gz',
'2018/01/01/XYZ1_20180101.gz'
'2018/01/02/XYZ1_20180102.gz']
for f in file_names:
print(f)
So, just testing this, I can find the files and print the strings just fine. Now, I'm trying to figure out how to load the contents of each file into a specific table in SQL Server. The thing is, I want to do a wildcard search for files that match a pattern, and load specific files into specific tables. So, I would like to do the following:
load all files with 'ABC' in the name, into my 'ABC_Table' and all files with 'XYZ' in the name, into my 'XYZ_Table' (all data starts on row 2, not row 1)
load the file name into a field named 'file_name' in each respective table (I'm totally fine with the entire string from 'file_names' or the part of the string after the last '/' character; doesn't matter)
I tried to use Azure Data Factory for this, and it can recursively loop through all files just fine, but it doesn't get the file names loaded, and I really need the file names in the table to distinguish which records are coming from which files & dates. Is it possible to do this using Azure Databricks? I feel like this is an achievable ETL process, but I don't know enough about ADB to make this work.
Update based on Daniel's recommendation
dfCW = sc.sequenceFile('/mnt/rawdata/2018/01/01/ABC%.gz/').toDF()
dfCW.withColumn('input', input_file_name())
print(dfCW)
Gives me:
com.databricks.backend.daemon.data.common.InvalidMountException:
What can I try next?
You can use input_file_name from pyspark.sql.functions
e.g.
withFiles = df.withColumn("file", input_file_name())
Afterwards you can create multiple dataframes by filtering on the new column
abc = withFiles.filter(col("file").like("%ABC%"))
xyz = withFiles.filter(col("file").like("%XYZ%"))
and then use regular writer for both of them.
In the workspace I have many m-files containing data I'd like to plot.
I have to read them all and save their plot without showing the results (I'll see them after all is done).
The last part can be done this way?
f = figure('Visible', 'off');
plot(x,y);
saveas(f,'figure.fig');
but I don't want to load manually each m-file where x and y are stored.
So I need a way to explore the filesystem and run these statements for each file, manipulate their name and save a jpg with the same name of its m-file.
The dir function will return a structure containing info on the Folders and Files in the current directory
>> FileInfo = dir
Then you need to write code to use that info to automatically navigate the directory structure (using cd for instance), and select the files you want to read.
The function what can also be useful if you're wanting to only look for certain file types, e.g. .mat files.
Not surprisingly, similar questions to this have been asked before, for instance see here