python – Batch retrieves address formatted with geometry (lat / long) and output in csv format

I have a csv file with 3 fields, two of which are of my interest, Merchant's name and City.
My goal was to generate several csv files each containing 6 fields, Merchant's name, City, first name, formaté_adresse, latitude, longitude.

For example, if an entry in the csv file is Starbucks, Chicago, I want the output csv to contain all the information in the 6 fields (as mentioned above) as
Starbucks, Chicago, Starbucks, "200 S Michigan Ave, Chicago, IL 60604, United States", 41.8164613, -87.8127855,
Starbucks, Chicago, Starbucks, "8 North Michigan Ave, Chicago, IL 60602, United States", 41.8164613, -87.8127855
and so on for the rest of the results.

For this, I used the text search query of the Google Maps Places API. Here is what I wrote.

import pandas as pd
# import googlemaps
import requests
# import csv
# import pprint in pp
from the hour of import sleep
randomly import


def search_output (search):
if len (data['results']) == 0:
print (No results were found for {}. & # 39; format (search))

other:

# Create a csv file
filename = search + & # 39; .csv & # 39;
f = open (file name, "w")

size_of_json = len (data['results'])

# Get the token from the next page
# if size_of_json = 20:
# next_page = data['next_page_token']

        for i in the range (size_of_json):
name = data['results'][i]['name']
            
            
            
            address = data['results'][i]['formatted_address']
            
            
            
            latitude = data['results'][i]['geometry']['location']['lat']
            
            
            
            longitude = data['results'][i]['geometry']['location']['lng']

            

            

            

            f.write (name.replace (& # 39;) & # 39;) + & # 39; + address.replace (& # 39;, & # 39;) ,, & # 39;) + & # 39;, & # 39; + str (latitude) + & # 39; + str (longitude) + & # 39;  n & # 39; ;)

f.close ()

print (& # 39; File successfully saved for "{}". format. (search))

to sleep (random.randint (120, 150))


API_KEY = & # 39; your_key_here & # 39;

PLACES_URL = https://maps.googleapis.com/maps/api/place/textsearch/json? & # 39;


# Make dataframe
df = pd.read_csv (& # 39; merchant.csv & # 39 ;, usecols =[0, 1])

# Build a search query
search_query = df['Merchant_Name'].astype (str) + & # 39; & # 39; + df['City']
search_query = search_query.str.replace (& # 39 ;, & # 39; + & # 39;)

random.seed ()

for search in search_query:
search_req = & # 39; query = {} & key = {} & # 39; format (search, API_KEY)
request = PLACES_URL + search_req

# Place the request and store the data in & # 39; data & # 39;
result = requests.get (request)
data = result.json ()

status = data['status']

    if the status == & # 39; OK & # 39 ;:
search_output (search)
status elif == & ZERO_RESULTS & # 39 ;:
print ("Zero results for" {} ". Moving on .. (format))
to sleep (random.randint (120, 150))
elif status == OVER_QUERY_LIMIT:
print (Limit the query reached! Try after a while. Can not complete the "{}". format. (search))
Pause
other:
print (status)
print (& # 39; ^ Status not okay, try again. Failed to complete "{}". format (search))
Pause

I want to implement the next page token but I can not think of a way that would not make everything mess. Another thing I want to improve is my CSV writing block. And dealing with redundancy.
I also plan to concatenate all csv files into one (while keeping the original separate files).

Please note that I am new to programming. In fact, it's actually one of my first programs to do something. So, please, elaborate a little more if need be. Thank you!

Python – multithreading + csv

I am trying to write CSV files using a thread for each file. The code goes through a list generated by groupby and calls a thread for each group.

By calling the function directly, the files are saved normally:

[save_csv(uf[0]+ '# .Csv', header, uf[1]) for uf in ufs]

With Threads, files are only saved with the header, even if the function is executed until the end.

[threading.Thread(target=(save_csv), args=(uf[0]+ '# .Csv', header, uf[1])). start () for uf in ufs

Save_csv function:

def save_csv (file, header, content):
with open (file, 'w', newline = 'encoding =' utf-8 ') as f:
writer = csv.DictWriter (f, delimiter = & # 39 ;;, field names = header)
writer.writeheader ()
writer.writerows (list (map (vars, content)))

Is there a reason for this to happen?

Note: The code uses the orientation of the object and the thread according to the requirements of the list of exercises.

migration – Migrating the csv date field to Drupal 8 using format_date

tried to import the date fields from a CSV file to Drupal 8 using the format_date function. followed these two answers
Import a CSV Date in the Date field
How to convert year to date with Migrate

but did not work for me. he continues to translate everything in 1969/12/31 19:33. Does anybody know why?

here is my YAML file where field_date is the name of the machine in the date field of my content type, and Date is the name of the field in CSV. and the date format in CSV format looks like 12/22/12 12:22

date of the field:
plugin: format_date
from_format: m / j / y H: i & # 39;
to_format: Y-m-d TH: i: s & # 39;
source: date

magento 1.9 – How to export a transaction ID and a credit card approval in CSV format

I wrote a script that exports a CSV file to a directory. The CSV contains the information of the order, once the order has been placed. The problem I encounter is the export of transaction ID information and CC approval. Does anyone have any idea on how to collect this information?

Below, you can see the two vars trying to collect $ paymentId and $ paymentApproval information.

public service sales_order_place_after ($ observer) {
$ order = $ observer-> getEvent () -> getOrder ();
$ quote_id = $ order-> getQuoteId ();

$ orderId = $ order-> getEntityId ();
$ order = Mage :: getModel ('sales / order') -> load ($ orderId);

$ text = "";
$ fp = fopen (hide / directory / for / question). $ order-> getIncrementId () .txt, # w;);

// invoice information
$ billingAddress = $ order-> getBillingAddress ();
$ countryCode = $ billingAddress-> getCountryId ();
$ country = Mage :: getModel ('directory / country') -> loadByCode ($ countryCode);
$ countryName = $ country-> getName ();

// ship to billing information
$ shippingAddress = $ order-> getShippingAddress ();
$ shippingAddressCountryCode = $ shippingAddress-> getCountryId ();
$ shippingAddressCountry = Mage :: getModel ('directory / country') -> loadByCode ($ shippingAddressCountryCode);
$ shippingAddressCountryName = $ shippingAddressCountry-> getName ();

$ query = "Collect";
$ shippingDescription = $ order-> getShippingDescription ();
if (substring ($ shippingDescription, 0, strlen ($ query)) !! == $ query) {
$ shippingCode = "PPD";
$ shippingAgentCode = "UPS";
$ shippingServiceCode = explode ("- UPS", $ shippingDescription)[1];
$ carrierAccountNumber = "";
$ shippingAmount = $ shippingAddress-> getShippingAmount ();
} other {
// Delivery information
$ shippingCode = "COL";
$ shippingAgentCode = "";
$ shippingServiceCode = "";

$ order_id = $ order-> getId ();

$ carrierAccountNumber = Mage :: getSingleton (& # 39; checkout / session & # 39;) -> getCollectmemoComment ();
$ carrierAccountNumber = $ carrierAccountNumber[$quote_id];
$ shippingAmount = 0;
}

// Order information
$ orderDate = $ order-> getCreatedAt ();
$ orderDateToShow = date (# n / j / Y), strtotime ($ orderDate));


// transaction information
$ orderTransactionDateTime = date (# n / j / y h: i: s A, strtotime ($ orderDate));

// Account information
$ userEmail = $ order-> getCustomerEmail ();

// Payment Information
$ payment = $ order-> getPayment ();
$ paymentCcType = $ payment-> getData (& # 39; cc_type & # 39;);
$ paymentPoNumber = $ payment-> getData (& # 39; po_number & # 39;);
$ paymentPoComment = $ payment-> getData (& # 39; po_comment & # 39;);
$ paymentIdTwo = $ payment-> getCcTransId ();
$ poRefferenceNumber = $ payment-> getData (& # 39; po_ref_number & # 39;);


$ paymentId = $ payment-> getCcTransId ();
// $ paymentId = $ payment-> getData (& # 39; cc_trans_id & # 39;);

$ paymentApproval = $ payment-> getData (& # 39; cc_approval & # 39;)
// $ paymentApproval = $ payment-> getCcApproval ();


$ paymentCcStatus = "";


if ($ paymentPoNumber! = "") {
$ paymentCcStatus;
} other {
$ paymentCcStatus = "approved";

}

if ($ paymentCcType == "VI") {
$ paymentCcType = "Visa";
} else if ($ paymentCcType == "MC") {
$ paymentCcType = "MasterCard";
} else if ($ paymentCcType == "AE") {
$ paymentCcType = "American Express";
} else if ($ paymentCcType == "DI") {
$ paymentCcType = "Discover";
}

$ item_incrementer = 1;
foreach ($ order-> getAllItems () as $ itemId => $ item) {
$ fields = array ($ billingAddress-> getCompany (), $ billingAddress-> getFirstname (), $ billingAddress-> getMiddlename (), $ billingAddress-> getLastname (), $ billingAddress-> getStreet (1), $ billingAddress-> getStreet (2), $ billingAddress-> getCity (), $ billingAddress-> getRegion (), $ billingAddress-> getPostcode (), $ countryName, $ billingAddress-> getTelephone (), $ userEmail, $ shippingAddress-> getCompany () , $ shippingAddress-> getFirstname (), $ shippingAddress-> getMiddlename (), $ shippingAddress-> getLastname (), $ shippingAddress-> getStreet (1), $ shippingAddress-> getStreet (2), $ shippingAddress-> getCity () , $ shippingAddress-> getRegion (), $ shippingAddress-> getPostcode (), $ shippingAddressCountryName, $ shippingAddress-> getTelephone (), $ shippingCode, $ shippingAgentCode, $ shippingServiceCode, $ carrierAccountNumber, $ orderActive>, getShippingAmount, $ paymentPoComment, $ poRefferenceNumber, $ order-> getIncrementId (), $ orderDateToShow, $ paymentC cStatus, $ paymentCcType, $ paymentApproval, $ paymentId, $ paymentIdTwo, $ order-> getGrandTotal (), $ orderTransactionDateTime, $ item_incrementer, $ item-> getSku (), $ item-> getQtyOrdered (), $ item-> getPrice () ));


// echo & # 39;
& # 39 ;, var_dump ($ fields), & # 39;

& # 39 ;;

$ field_incrementer = 0;
foreach ($ fields as $ field) {
if ($ field_incrementer> 0) {
$ text. = " t";
}
$ text. = $ field;

$ field_incrementer ++;
}

$ text. = " r n";

$ item_incrementer ++;
}

fwrite ($ fp, $ text);

fclose ($ fp);
}

How can I get the inflation rate for cryptographic coins in csv format

I am interested in econometric analysis of crypto-coins and returns according to the rate of inflation. All CSV files that have all the parts and the rate of inflation? Thank you.

excel – Extracting data from very large csv files

I have a 40 GB csv file with over 60 million rows for data analysis. Each line has a unique identifier (some numbers). For example, the unique identifier of the first line will repeat approximately 150,000 lines later.

I would like to have a method to browse the whole file, extract the lines with the same identifier and write them in new csv files. Is there a good automated way to do it? Please note that the file is very large and that Excel has problems opening it.

CSV to vcf contact | Talk Web Hosting

CSV to vcf contact | Talk Web Hosting

& # 39;);
var sidebar_align = & # 39; right & # 39 ;;
var content_container_margin = parseInt (& # 39; 350px & # 39;);
var sidebar_width = parseInt (& # 39; 330px & # 39;)
// ->

  1. CSV to vcf contact

    Hi guys,
    I want to know if it is possible to convert my comma-separated value to a vcf contact file?
    Help me, please.
    Thanks in advance ….


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Problem with data mining, space in the .csv file

insert the description of the image hereHello, I'm doing data mining work on public archives. The file I am working on is a .csv file of just over 11 million lines and about 119 columns. My problem is this: as the picture shows, the file contains statistics for several groups of people. The columns shown in the image refer to the list of disabilities that this person may have: be it auditory, visual, mental or otherwise. When the person does not have a disability must have the value "0" and when it must have the value "1". Most files contain these blank records.
I've found a certain pattern in these columns: whenever a record appears (as shown in the pictures), there is at least a number '1'. indicating the disability of this person, the rest is filled with "0". Would it then be possible to conclude that the one who created the file left blanks so as not to fill in zeros and save the file size?
I am processing the data and I need to know if I can just fill those spaces.
Is there a standard in .csv files or in data mining that can serve as an explanation for so many empty spaces?

javascript – JSON flattening with object duplication on the array property for CSV generation

I'm looking for a way to turn JSON data into a flat data object, similar to a "csv" file. In a way, I am looking to "sqlirize" a mongodb collection. I have already checked some json flat libraries in NPM, but none of them has solved my problem entirely. I resolved it in my way but wanted to know if there was a more effective way.

I have a collection that presents the data via an API in the following way:

[{
    "data": {
        "name": "John",
        "age": 23,
        "friends": [{
            "name": "Arya",
            "age": 18,
            "gender": "female"
        }, {
            "name": "Sansa",
            "age": 20,
            "gender": "female"
        }, {
            "name": "Bran",
            "age": 17,
            "gender": "male"
        }]
    
    
    
    }
}, {
"The data": {
"name": "Daenerys",
"age": 24 years,
"friends": [{
            "name": "Grey Worm",
            "age": 20,
            "gender": "male"
        }, {
            "name": "Missandei",
            "age": 17,
            "gender": "female"
        }]
    }
}]

This is the function I created to update a flattened secure json (for example, everything is flat except the tables).

const {cloneDeep} = require (& lodash & # 39;)
const flatten = require (& # 39; flat & # 39;)

const reflatten = (items) => {
const repulsed = []

  items.forEach (item => {
let array = false

for (const key of Object.keys (item)) {
if (Array.isArray (item[key])) {
array = true

const children = Array (item[key].length) .fill (). map (() => cloneDeep (item))

for (i = 0; i < children.length; i++) {
          const keys = Object.keys(children[i][key][i])

          keys.forEach(k => {
children[i][`${key}.${k}`]    = children[i][key][i][k]
          
          
          
          })
delete children[i][key]
          
          
          
          reflatted.push (children[i])
}
Pause
}
}
if (! array) {
reflatted.push (item)
}
})

returns reflatted.length === items.length
? pushed
: redone (redone)
}

lines const = []

for (constant element) {
const flat = [flatten(item)]

  rows.push (... reflatten (flat)]}

console.log (lines)

The expected (and current) output is as follows:

[{
    "data.name": "John",
    "data.age": 23,
    "data.friends.name": "Arya",
    "data.friends.age": 18,
    "data.friends.gender": "female"
}, {
    "data.name": "John",
    "data.age": 23,
    "data.friends.name": "Sansa",
    "data.friends.age": 20,
    "data.friends.gender": "female"
}, {
    "data.name": "John",
    "data.age": 23,
    "data.friends.name": "Bran",
    "data.friends.age": 17,
    "data.friends.gender": "male"
}, {
    "data.name": "Daenerys",
    "data.age": 24,
    "data.friends.name": "Grey Worm",
    "data.friends.age": 20,
    "data.friends.gender": "male"
}, {
    "data.name": "Daenerys",
    "data.age": 24,
    "data.friends.name": "Missandei",
    "data.friends.age": 17,
    "data.friends.gender": "female"
}]

Although I have achieved the expected result, I still wonder if there are other libraries or there is a more efficient way to do it.

postgresql – Best practices for storing multiple SQL tables per user (built from a loaded CSV file?)

I have some users who will download some types of predefined CSV files. I need to store this data as SQL tables to execute queries on this data for a particular user. However, this means that there can potentially be loads of tables.

Right now, I am storing the CSV in an S3 compartment with the key username / filename / timestamp-filename.csv

What would be the best way to turn it into SQL? The CSV analysis is performed using fast-csv nodes, it's just the baffling diagram.

I was thinking of table names like User-nom-nom-data-file so each user will have as many tables as csv categories, or 10 tables each. Would it be better to store this in a separate database or in the same database, simply by differentiating itself by the name / prefix of the table?

This is a backoffice application with only a few users.

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