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import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import torch.optim as optim
import torch.nn.functional as F
from torchvision import transforms, utils, datasets

import torch

porimol

This is an unofficial manual for the couchdb Python module I wish I had had.

Installation

pip install couchdb

Importing the module

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#!/usr/bin/env python
# http://www.rabbitmq.com/tutorials/tutorial-two-python.html
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

message = ' '.join(sys.argv[1:]) or "Hello World!"

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['dhur', 'teri', 'eidaki', 'oops', 'ahh'],
    'last_name': ['modhu', 'kodu', 'mula', 'kodu', 'modhu'],
    'phone_number': ['+8801820050440', '+8801820050444', '+8801820050440', '+8801820050441', '+8801820050440']
})

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import pandas as pd

data = pd.read_csv('test.csv')
# array3 = data['Column2'].replace(np.NaN,-1)
# length = array3.shape[0]
# print(length)
# arr3 = [];
# for i in range(length):
#     if array3[i] != -1:
# #         print(i)

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brand.csv
-----------------------------------
bid,brand,web_site
1,sqaure,sqaure.com.bd
2,beximco,beximco.com.bd

product.csv
-----------------------------------
pid,brand_id,brand,product_name,address
1,null,sqaure,napa extra,null

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#include<stdio.h>
#include<string.h>

void search(char *pat, char *txt)
{
    int i = 0;
    int M = strlen(pat);
    int N = strlen(txt);

    // A loop to slide pat[] one by one