I am trying to read an excel file (xlsx) into a data frame in ibm watson studio. the excel file is saved in my list of assets. i'm a bit new to python
i have tried creating a project token with some help i got here. I will appreciate if someone helps with the complete code.
i tried this
from project_lib import Project
project = Project(project_id='',
project_access_token='')
pc = project.project_context
file = project.get_file("xx.xlsx")
file.sheet_names
df = pd.ExcelFile(file)
df = file.parse (0)
df.head ()
i needed to pass the excel file into a pandas data frame , pd for eg.
All you need to do is
First insert the project token as you already did.
Then simply fetch file and then do .seek(0),
Then read it using pandas' read_excel() and you should be able to read it.
# Fetch the file
my_file = project.get_file("tests-example.xls")
# Read the CSV data file from the object storage into a pandas DataFrame
my_file.seek(0)
import pandas as pd
pd.read_excel(my_file, nrows=10)
For more information:- https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/project-lib-python.html
Related
I'm a newbie to beautiful soup. Can anyone suggest how to scrape the excel file for the past 14 days? My understanding is to loop over the date and save the file. Thanks
https://www.hkexnews.hk/reports/sharerepur/sbn.asp
import requests
from bs4 import BeautifulSoup
res=requests.get("https://www.hkexnews.hk/reports/sharerepur/sbn.asp")
soup=BeautifulSoup(res.text,"lxml")
Now we will find data inside table using find method and use find_all to get all td tags and append data to list lst.
main_data=soup.find("table").find_all("td")
lst=[]
for data in main_data:
try:
url=data.find("a").get('href')[1:]
main_url="https://www.hkexnews.hk/reports/sharerepur"+url
lst.append(main_url)
except AttributeError:
pass
Now iterate through lst and call individual URL to download data to excel file.
for url in range(len(lst)):
resp=requests.get(lst[url])
output = open(f'test_{url}.xls', 'wb')
output.write(resp.content)
output.close()
print(url)
Image: (File being created in Local)
I want to read csv file from google cloud storage with a function similar to
read.csv.
I used library googleCloudStorageR and I can't find a function for that. I don't want to download it, I just want to read it in environment like a data frame.
If you download a .csv file, googleCloudStorageR will by default put it into a data.frame for you via write.csv - you can turn off the behaviour by specifying saveToDisk
# will make a data.frame
gcs_get_object("mtcars.csv")
# save to disk as a CSV
gcs_get_object("mtcars.csv", saveToDisk = "mtcars.csv")
You can specify your own parse function by supplying it via parseFunction
## default gives a warning about missing column name.
## custom parse function to suppress warning
f <- function(object){
suppressWarnings(httr::content(object, encoding = "UTF-8"))
}
## get mtcars csv with custom parse function.
gcs_get_object("mtcars.csv", parseFunction = f)
I’ve tried running a sample csv file with the as.data.frame() function.
In order to run this code snippet make sure you install (install.packages("data.table")) and included the library library(“data.table”)
Also be sure that you include the fread() within the as.data.frame() function in order to read the file from it’s location.
Here is the code snippet I ran and managed to display the data frame for my data set:
library(“data.table”)
MyData <- as.data.frame(fread(file="$FILE_PATH",header=TRUE, sep = ','))
print(MyData)
Reading Data with TensorFlow:
There is one other way you can read a csv from your cloud storage with the TensorFlow API. I would assume you are accessing this data from a bucket? Firstly, you would need to install the “readr” and “cloudml” packages for these functionalities to work. Then you would need to use gs_data_dir(“gs://your-bucket-name”) along with specifying the file path file.path(data_dir, “something.csv”). You would then want to read data from the file path with read_csv(file.path(data_dir, “something.csv”)). If you want it formatted as a data frame it should look something like this.
library(“data.table”)
library(cloudml)
library(readr)
data_dir <- gs_data_dir(“gs://your-bucket-name”)
MyData <- as.data.frame(read_csv(file.path(data_dir, “something.csv”)))
print(MyData)
Make sure you have properly authenticated access to your storage
More information in this link
Introdution
I have written following R code by referring Link-1. Here, Sparklyr package is used in R programming to read huge data from JSON file. But, while creating CSV file, it has shown the error.
R code
sc <- spark_connect(master = "local", config = conf, version = '2.2.0')
sample_tbl <- spark_read_json(sc,name="example",path="example.json", header = TRUE,
memory = FALSE, overwrite = TRUE)
sdf_schema_viewer(sample_tbl) # to create db schema
sample_tbl %>% spark_write_csv(path = "data.csv") # To write CSV file
Last line shows the following error. Dataset contains different data types. If required I can show the database schema. It contains nested data columns.
Error
Error: java.lang.UnsupportedOperationException: CSV data source does not support struct,media:array,display_url:string,expanded_url:string,id:bigint,id_str:string,indices:array,media......
Question
How to resolve this error? Is it due to the different data types or deep level 2 to 3 nested columns? Any help would be appreciated.
It seems that your dataframe has array data type, which is NOT supported by CSV. It seems it's not possible that CSV file can include array or other nest structure for this scenario.
Therefore, If you want your data to be human readable text, write out as Excel file.
Please note that Excel CSV (very special case though) supports arrays in CSV using "\n"
inside quotes, but you have to use as EOL for the row "\r\n" (Windows EOL).
I am using googledrive package from CRAN. But, function - drive_upload lets you upload a local file and not a data frame. Can anybody help with this?
Just save a data_frame in question to a local file. Most basic options would be saving to CSV or saving an RData.
Example:
test <- data.frame(a = 1)
tempFileCon <- file()
write.csv(test, file = tempFileCon)
rm(test)
load("test.Rds")
exists("test")
Since clarified it is not possible to use temporary file we could use a file connection.
test <- data.frame(a = 1)
tempFileCon <- file()
write.csv(test, file = tempFileCon)
And now we have the file conneciton in memory that we can use to provide for other functions. Caveat - use literal object name to address it and not quotations like you would with actual files.
Unfortunately I can find no way to push the dataframe up directly, but just to document for others trying to get the basics accomplished that this question touches upon is with the following code that writes a local .csv and then bounces it up through tidyverse::googledrive to express itself as a googlesheet.
write_csv(iris, 'df_iris.csv')
drive_upload('df_iris.csv', type='spreadsheet')
You can achieve this using gs_add_row from googlesheets package. This API accepts dataframes directly as input parameter and uploads data to the specified google sheet. Local files are not required.
From the help section of ?gs_add_row:
"If input is two-dimensional, internally we call gs_add_row once per input row."
This can be done in two ways. Like mentioned by others, a local file can be created and this can be uploaded. It is also possible to create a new spreadsheet in your drive. This spreadsheet will be created in the main folder of your drive. If you want it stored somewhere else, you can move it after creation.
# install the packages
install.packages("googledrive", "googlesheets4")
# load the libraries
library(googledrive)
library(googlesheets4)
## With local storage
# Locally store the file
write.csv(x = iris, file = "iris.csv")
# Upload the file
drive_upload(media = "iris.csv", type='spreadsheet')
## Direct storage
# Create an empty spreadsheet. It is stored as an object with a sheet_id and drive_id
ss <- gs4_create(name = "my_spreadsheet", sheets = "Sheet 1")
# Put the data.frame in the spreadsheet and provide the sheet_id so it can be found
sheet_write(data=iris, ss = ss, sheet ="Sheet 1")
# Move your spreadsheet to the desired location
drive_mv(file = ss, path = "my_creations/awesome location/")
I have the basic setup done following the link below:
http://htmlpreview.github.io/?https://github.com/Microsoft/AzureSMR/blob/master/inst/doc/tutorial.html
There is a method 'azureGetBlob' which allows you to retrieve objects from the containers. however, it seems to only allow "raw" and "text" format which is not very useful for excel. I've tested the connections and etc, I can retrieve .txt / .csv files but not .xlsx files.
Does anyone know any workaround for this?
Thanks
Does anyone know any workaround for this?
There is no file type on the azure blob storage, it is just a blob name. The extension type is known for OS. If we want to open the excel file in the r, we could use the 3rd library to do that such as readXl.
Work around:
You could use the get blob api to download the blob file to local path then use readXl to read the file. We also get could more demo code from this link.
# install
install.packages("readxl")
# Loading
library("readxl")
# xls files
my_data <- read_excel("my_file.xls")
# xlsx files
my_data <- read_excel("my_file.xlsx")
Solved with the following code. Basically, read the file in byte then wrote the file to disk then read it into R
excel_bytes <- azureGetBlob(sc, storageAccount = "accountname", container = "containername", blob=blob_name, type="raw")
q <- tempfile()
f <- file(q, 'wb')
writeBin(excel_bytes, f)
close(f)
result <- read.xlsx(q, sheetIndex = sheetIndex)
unlink(q)