There are different lists available in pelicanconf.py such as
SOCIAL = (('Facebook','www.facebook.com'),)
LINKS =
etc.
I want to manage these content and create my own lists by loading these values from an external file which can be edited independently. I tried importing data as a text file using python but it doesn't work. Is there any other way?
What exactly did not work? Can you provide code?
You can execute arbitrary python code in your pelicanconf.py.
Example for a very simple CSV reader:
# in pelicanconf.py
def fn_to_list(fn):
with open(fn, 'r') as res:
return tuple(map(lambda line: tuple(line[:-1].split(';')), res.readlines()))
print(fn_to_list("data"))
CSV file data:
A;1
B;2
C;3
D;4
E;5
F;6
Together, this yields the following when running pelican:
# ...
((u'A', u'1'), (u'B', u'2'), (u'C', u'3'), (u'D', u'4'), (u'E', u'5'), (u'F', u'6'))
# ...
Instead of printing you can also assign this list to a variable, say LINKS.
Related
So I have two files and I need to create an output file for them - my prof. wants me to use a while loop to process the first file and a for loop for the second file, as well as use a try/except block to read from the input and write to the output. I think I have like, a general idea for the initial code but I'm still lost.
#reads the file
n1 = open('nameslist1.txt', 'r')
n2 = open('nameslist2.txt', 'r')
print(n1.read())
print(n2.read())
n1.close()
n2.close()
#writes the file
n1_o = open('allnames.txt', 'w')
n2_o = open('allnames.txt', 'w')
n1_o.write('nameslist1.txt')
n2_o.write('nameslist2.txt')
n1_o.close()
n2_o.close()
I'm having trouble finding an example to follow online for this simple use-case:
Load a CSV file from an s3 object location to julia DataFrame.
Here is what I tried that didn't work:
using AWSS3, DataFrames, CSV
filepath = S3Path("s3://muh-bucket/path/data.csv")
CSV.File(filepath) |> DataFrames # fails
# but I am able to stat the file
stat(filepath)
#=
Status( mode = -rw-rw-rw-,
...etc
size = 2141032 (2.0M),
blksize = 4096 (4.0K),
blocks = 523,
mtime = 2021-09-01T23:55:26,
...etc
=#
I can also read the file to a string object locally:
data_as_string = String(AWSS3.read(filepath);
#"column_1\tcolumn_2\tcolumn_3\t...etc..."
My AWS config is in order, I can access the object from julia locally.
How to I get this into a dataframe?
Thanks to help from the nice people on julia slack channel (#data).
bytes = AWSS3.read(S3Path("s3://muh-bucket/path/data.csv"))
typeof(bytes)
# Vector{UInt8} (alias for Array{UInt8, 1})
df = CSV.read(bytes, DataFrame)
Bingo, I'm in business. The CSV.jl maintainer mentions that S3Path types used to work when passed to CSV.read, so perhaps this will be even simpler in the future.
Helpful SO post for getting AWS configs in order
I have a snakemake rule aggregating several result files to a single file, per study. So to make it a bit more understandable; I have two roles ['big','small'] that each produce data for 5 studies ['a','b','c','d','e'], and each study produces 3 output files, one per phenotype ['xxx','yyy','zzz']. Now what I want is a rule to aggregate the phenotype results from each study to a single summary file per study (so merging the phenotypes into a single table). In the merge_results rule I give the rule a list of files (per study and role), and aggregate these using a pandas frame, and then spit out the result as a single file.
In the process of merging the results I need the 'pheno' variable from the input file being iterated over. Since pheno is not needed in the aggregated output file, it is not provided in output and as a consequence it is also not available in the wildcards object. Now to get a hold of the pheno I parse the filename to grab it, however this all feels very hacky and I suspect there is something here I have not understood properly. Is there a better way to grab wildcards from input files not used in output files in a better way?
runstudy = ['a','b','c','d','e']
runpheno = ['xxx','yyy','zzz']
runrole = ['big','small']
rule all:
input:
expand(os.path.join(output, '{role}-additive', '{study}', '{study}-summary-merge.txt'), role=runrole, study=runstudy)
rule merge_results:
input:
expand(os.path.join(output, '{{role}}', '{{study}}', '{pheno}', '{pheno}.summary'), pheno=runpheno)
output:
os.path.join(output, '{role}', '{study}', '{study}-summary-merge.txt')
run:
import pandas as pd
import os
# Iterate over input files, read into pandas df
tmplist = []
for f in input:
data = pd.read_csv(f, sep='\t')
# getting the pheno from the input file and adding it to the data frame
pheno = os.path.split(f)[1].split('.')[0]
data['pheno'] = pheno
tmplist.append(data)
resmerged = pd.concat(tmplist)
resmerged.to_csv(output, sep='\t')
You are doing it the right way !
In your line:
expand(os.path.join(output, '{{role}}', '{{study}}', '{pheno}', '{pheno}.summary'), pheno=runpheno)
you have to understand that role and study are wildcards. pheno is not a wildcard and is set by the second argument of the expand function.
In order to get the phenotype if your for loop, you can either parse the file name like you are doing or directly reconstruct the file name since you know the different values that pheno takes and you can access the wildcards:
run:
import pandas as pd
import os
# Iterate over phenotypes, read into pandas df
tmplist = []
for pheno in runpheno:
# conflicting variable name 'output' between a global variable and the rule variable here. Renamed global var outputDir for example
file = os.path.join(outputDir, wildcards.role, wildcards.study, pheno, pheno+'.summary')
data = pd.read_csv(file, sep='\t')
data['pheno'] = pheno
tmplist.append(data)
resmerged = pd.concat(tmplist)
resmerged.to_csv(output, sep='\t')
I don't know if this is better than parsing the file name like you were doing though. I wanted to show that you can access wildcards in the code. Either way, you are defining the input and output correctly.
i'm trying to read a partitionned dataset in aws s3, it looks like :
MyDirectory--code=1--file.parquet
--code=2--another.parquet
--code=3--another.parquet
i created a file_list containing the path to all the files in the directory then executed
df = pq.ParquetDataset(file_list, filesystem=fs).read().to_pandas()
everything works except that the partition column code doesn't exist in the dataframe df.
i tried it also using one path to MyDirectory insted of file_list, but found an error
"Found files in an intermediate directory: s3://bucket/Mydirectoty", i can't find any answer online.
Thank you!
AWS has a project (AWS Data Wrangler) that helps with the integration between Pandas/PyArrow and their services.
This snippet should work:
import awswrangler as wr
# Write
wr.s3.to_parquet(
df=df,
path="s3://...",
mode="overwrite",
dataset=True,
database="my_databse", # Optional, only if you want it available on Athena/Glue Catalog
table="my_table",
partition_cols=["PARTITION_COL_NAME"])
# READ
df = wr.s3.read_parquet(path="s3://...", dataset=True)
If you're happy with other tools you can give dask a try. Assume all the data you want to read is in s3://folder you can just use
import dask.dataframe as dd
storage_options = {
'key': your_key,
'secret': your_secret}
df = dd.read_parquet("s3://folder",
storage_options=storage_options)
I'm trying to automate writing CSV files to an RSQLite DB.
I am doing so by indexing csvFiles, which is a list of data.frame variables stored in the environment.
I can't seem to figure out why my dbWriteTable() code works perfectly fine when I enter it manually but not when I try to index the name and value fields.
### CREATE DB ###
mydb <- dbConnect(RSQLite::SQLite(),"")
# FOR LOOP TO BATCH IMPORT DATA INTO DATABASE
for (i in 1:length(csvFiles)) {
dbWriteTable(mydb,name = csvFiles[i], value = csvFiles[i], overwrite=T)
i=i+1
}
# EXAMPLE CODE THAT SUCCESSFULLY MANUAL IMPORTS INTO mydb
dbWriteTable(mydb,"DEPARTMENT",DEPARTMENT)
When I run the for loop above, I'm given this error:
"Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") :
cannot open file 'DEPARTMENT': No such file or directory
# note that 'DEPARTMENT' is the value of csvFiles[1]
Here's the dput output of csvFiles:
c("DEPARTMENT", "EMPLOYEE_PHONE", "PRODUCT", "EMPLOYEE", "SALES_ORDER_LINE",
"SALES_ORDER", "CUSTOMER", "INVOICES", "STOCK_TOTAL")
I've researched this error and it seems to be related to my working directory; however, I don't really understand what to change, as I'm not even trying to manipulate files from my computer, simply data.frames already in my environment.
Please help!
Simply use get() for the value argument as you are passing a string value when a dataframe object is expected. Notice your manual version does not have DEPARTMENT quoted for value.
# FOR LOOP TO BATCH IMPORT DATA INTO DATABASE
for (i in seq_along(csvFiles)) {
dbWriteTable(mydb,name = csvFiles[i], value = get(csvFiles[i]), overwrite=T)
}
Alternatively, consider building a list of named dataframes with mget and loop element-wise between list's names and df elements with Map:
dfs <- mget(csvfiles)
output <- Map(function(n, d) dbWriteTable(mydb, name = n, value = d, overwrite=T), names(dfs), dfs)