I have seen multiple posts on exporting ngx-datatable to csv/xlsx. However, I did not come across any post which says Import Excel file into ngx-datatable which is basically what I need. I need to read an excel file that user uploads and display into ngx-datatable (so basically excel file acting as source for ngx-datatable)
Any guidelines / help links to proceed will be a great help.
If you can transform into an csv file, there is this lib called ngx-csv-parser (https://www.npmjs.com/package/ngx-csv-parser) that helps to format data in object array, the way you need to send to a ngx-datatable. It says it is designed for Angular 13 but has compability with previous versions. I've tested it in Angular 10 and it does work.
It has a setting to use header or no. If you do, you can shape your columns prop from ngx-datatable with the same name of the header.
Example:
Lets say you have a csv file like this:
ColumnA,ColumnB,ColumnC
a,b,c
The output of using this lib (the way it is said in its readme) with header= true will be:
csvRecords = [{ColumnA: a, ColumnB: b, ColumnC: c}]
Lets say you also have an array of columns:
columns = [
{name="A", prop: ColumnA},
{name="B", prop: ColumnB},
{name="C", prop: ColumnC}
]
Then use columns and csvRecords in your html.
<ngx-datatable
class="material"
[rows]="csvRecords"
[columns]="columns"
>
</ngx-datatable>
Your table will be filled with data from your csv.
Related
I extracted the twitter data to the R script via packages (rtweet) and (tidyverse) through spesific hashtag. After sucessfully get the data, I need to subset some of the variables that I want to analyze. I code the subset function and console shows the subsetted variables. Despite this, when I tried to write this to the csv file, written csv shows the whole variables instead to show only subsetted variables. Codes that I entered as follows.
twitter_data_armenian_issue_iki <- search_tweets("ermeniler", n=1000, include_rts = FALSE)
view(twitter_data_armenian_issue_iki)
twitter_data_armenian_issue_iki_clean <- cbind(twitter_data_armenian_issue_iki, users_data(twitter_data_armenian_issue_iki)[,c("id","id_str","name", "screen_name")])
twitter_data_armenian_issue_iki_clean <-twitter_data_armenian_issue_iki_clean[,! duplicated(colnames(twitter_data_armenian_issue_iki_clean))]
view(twitter_data_armenian_issue_iki_clean)
data_bir <-data.frame(twitter_data_armenian_issue_iki_clean)
data_bir[ , c("created_at", "id", "id_str", "full_text", "name", "screen_name", "in_reply_to_screen_name")]
write.csv(data_bir, "newdata.csv", row.names = FALSE, )
If anyone want to help me, I will be more pleased. Thank you
I tried to get twitter data with only some spesific columns that I want to analyze. In order to do this, I entried the subset function and run. But when I tried to write this to the csv, written csv file shows the wole variable. I controlled the environment panel, I could not see the subsetted data.
My question is How can I add the subsetted data to the environment and write this to the csv without any error.
I frequent a real estate website that shows recent transactions, from which I will download data to parse within a Pandas dataframe. Everything about this dataset remains identical every time I download it (regarding the column names, that is).
The name of the Excel output may change, though. For example, if I already have download a few of these in my Downloads folder, the file that's exported may read "Generic_File_(3)" or "Generic_File_(21)" if I already have a few older "Generic_File" exports in that folder from a previous export.
Ideally, I'd like my workflow to look like this: export this Excel file of real estate sales, then run a Python script to read in the most recent export as a Pandas dataframe. The catch is, I don't want to have to go in and change the filename in the script to match the appending number of the Excel export everytime. I want the pd.read_excel method to simply read the "Generic_File" that is appended with the largest number (which will obviously correspond to the most rent export).
I suppose I could always just delete old exports out of my Downloads folder so the newest, freshest export is always named the same ("Generic_File", in this case), but I'm looking for a way to ensure I don't have to do this. Are wildcards the best path forward, or is there some other method to always read in the most recently downloaded Excel file from my Downloads folder?
I would use the OS package and create a method to read to file names in the downloads folder. Parsing string filenames you could then find the file following your specified format with the highest copy number. Something like the following might help you get started.
import os
downloads = os.listdir('C:/Users/[username here]/Downloads/')
is_file = [True if '.' in item else False for item in downloads]
files = [item for keep, item in zip(is_file, downloads) if keep]
** INSERT CODE HERE TO IDENTIFY THE FILE OF INTEREST **
Regex might be the best way to find matches if you have a diverse listing of files in your downloads folder.
you may have noted that this is a long question, that was because I really put an effort to explain how many WTF's I am facing, and, maybe, is not that good yet, anyway, I appreciate your help!
Context
I'm doing an integration project for a client that handles a bunch of data to generate Excel files in .xls format, notice that extension!
While developing the project I was using the xlrd and xlwt python extensions, because, again, I need to create a .xls file. But at some time I had to download and extract a file and was in .csv format (but, in reality, the file contains an HTML table :c).
So I decided to use padas to read the HTML, create a data frame so I can manipulate and return a .xls excel file.
The Problem
after coding the logic and checking that the data was correct, I tried to upload this file to the e-commerce plataform.
What happened is that the platform doesn't validate my archive.
First I will briefly explain how the site work: He accepts .xls and only .xls file, probably manipulate and use them to update the database, I have access to nothing from the code source.
When I upload the file, the site takes me to a configuration page where, if I want or the site didn't relate right, I could relate excel columns to be the id or values that would be updated on the database.
The 'generico4' field expects 'smallint(5) unsigned' on the type.
An important fact is that I sent the file to my client so he could validate the data, and after many conversations between us was discovered that if he, just by downloading my file, opening, and saving, the upload works fine (the second image from my slide), important to note that he has a MacBook and me, Ubuntu. I tried to do the same thing but not worked.
He sent me this file and I tried to see the difference between both and I found nothing, the type of the numbers are the same, that is 'float', and printed via excel with the formula =TYPE(cell) returned 1.
I already tried many other things but nothing works :c
The code
Follow the code so you can have an idea of the logic
def stock_xls(data_file_path):
# This is my logic to manipulate the data
df = pd.read_html(data_file_path)[0]
df = df[[1,2]]
df.rename(columns={1:'sku', 2:'stock'}, inplace=True)
df = df.groupby(['sku']).sum()
df.reset_index(inplace=True)
df.loc[df['stock'] > 0, 'stock'] = 1
df.loc[df['stock'] == 0, 'stock'] = 2
# I create a new Worbook (via pandas was not working too)
wb_out = xlwt.Workbook()
ws_out = wb_out.add_sheet(sheetname='stock')
# Set the columns name
ws_out.write(0, 0, 'sku')
ws_out.write(0, 1, 'generico4')
# Copy DataFrame data to the WorkBook
for index, value in df.iterrows():
ws_out.write(index + 1, 0, str(value['sku']))
ws_out.write(index + 1, 1, int(value['stock']))
path = os.path.join(BASE_DIR, f'src/xls/temp/')
Path(path).mkdir(parents=True, exist_ok=True)
file_path = os.path.join(path, "stock.xls")
wb_out.save(file_path)
return file_path
I took a corpus of text and put it into VosViewer to create a network for me. When I import this .net file into gephi, it works fine: I get a semantic network. Though I'm a little stuck for what attributes to select to import into cytoscape. Here is a CSV file of the network (.net import wouldn't work), and I just need to know which column to select as what.
Each column has the option of being imported as one of the following:
Source Node
Interaction type
Edge Attribute
Source Node Attribute
Target Node Attribute
An option would be to export your network from Gephi in GraphML format and then load it from Cytoscape. This will allow you to forget about the gory conversion details
Just go to File>Export>Graph file and select GraphML under the Files of type: option
Is this the .net format you are using?
https://gephi.org/users/supported-graph-formats/pajek-net-format/
If so, then you can see that the format (like any network file format) requires at least two columns of node identifiers, i.e., source and target. Your screenshot clearly shows the first columns "id" along with some node attributes. The column headers for "weight" and "cluster" are not as clear. My naive guess is that the "weight (occurrences)" column is actually the second list of "target" node ids and the "weight (co-occurrences)" is an edge attribute. But this is just a guess!
If you can deduce the meaning of the columns in your file, then it's a simple matter to assign and import them into Cytoscape for .net or any tabluar file format.
Hope this helps!
I am totally new to Stata and am wondering how to import .xlsx data in Stata. Let's say the data is in the subdirectory Data and has name "a b c.xlsx". So, from working directory, the data is in /Data
I am trying to do
import excel using "\Data\a b c.xlsx", sheet("a")
but it's not working
it's not working
is anything but a useful error report. For future questions, please report the exact error given by Stata.
Let's say the file is in the directory /home/roberto then
clear
set more off
import excel using "/home/roberto/a b c.xlsx"
list
should work.
If you are already in /home/roberto (which you can verify using display c(pwd)), then
import excel using "a b c.xlsx"
should work.
Using backslashes to refer to directories is not encouraged. See Stata tip 65: Beware the backstabbing backslash, by Nick Cox.
See also help cd.