cameronShadmehry

# Specify the stock that you want to analyze.
stock = 'TSLA'

# Request for data from Finhub.io (30 calls per second limit: https://finnhub.io/docs/api/rate-limit).
r = requests.get('https://finnhub.io/api/v1/stock/insider-transactions?symbol='+stock+'&token=YOUR API KEY')

# Load the JSON file as a string.
test = json.loads(r.text)

samarth-agrawal-86

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)

from sklearn.model_selection import train_test_split

# Let's say we want to split the data in 80:10:10 for train:valid:test dataset
train_size=0.8

X = df.drop(columns = ['SalePrice']).copy()

asaboor-gh

import numpy as np
import PIL
from io import BytesIO
import matplotlib.pyplot as plt

def plt_to_text(plt_fig=None,width=144,vscale=0.96,colorful=True,invert=False,crop=False,outfile=None):
    """Displays matplotlib figure in terminal as text. You should use a monospcae font like `Cascadia Code PL` to display image correctly. Use before plt.show(). 
    - **Parameters**
        - plt_fig: Matplotlib's figure instance. Auto picks if not given.
        - width  : Character width in terminal, default is 144. Decrease font size when width increased.

betterdatascience

# Add imputed values as columns to the untouched dataset
iris_orig['MF_sepal_length'] = X_imputed[:, 0]
iris_orig['MF_petal_width'] = X_imputed[:, -1]
comparison_df = iris_orig[['sepal_length', 'MF_sepal_length', 'petal_width', 'MF_petal_width']]

# Calculate absolute errors
comparison_df['ABS_ERROR_sepal_length'] = np.abs(compaison_df['sepal_length'] - comparison_df['MF_sepal_length'])
comparison_df['ABS_ERROR_petal_width'] = np.abs(compaison_df['petal_width'] - comparison_df['MF_petal_width'])

# Show only rows where imputation was performed

CamDavidsonPilon

SajidLhessani

import pandas as pd
from pandas_datareader import data, wb
import datetime

start = pd.to_datetime('2020-02-04')
end = pd.to_datetime('today')

tesla_df = data.DataReader('TSLA', 'yahoo', start , end)
tesla_df

qdm12

Wireguard and iptables restrictions for multiple users

If you don't know what Wireguard is, well, you should. It's fast, easy to setup and highly configurable. We will configure Wireguard for multiple users with various restrictions using iptables.

Assumptions

This should fit most setups (not mine though 😉)

felixlohmeier

How to control OpenRefine 3.3+ with cURL (and jq) in Bash scripts

tested on Fedora 32 with bash 5.0.17 and curl 7.69.1

Quick start

1. Clone this gist

git clone https://gist.github.com/d76bd27fbc4b8ab6d683822cdf61f81d.git bash-refine

lakshmanok

PROJECT='ai-analytics-solutions'
BUCKET='ai-analytics-solutions-kfpdemo'
REGION='us-central1'

from datetime import datetime
import apache_beam as beam

def parse_nlp_result(response):
    return [
        # response, # entire string

batpigandme

library(tidyverse)
set.seed(406)
df <- tibble(x = runif(3), y = runif(3), z = runif(3))
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
df %>% mutate(m = pmin(x, y, z))
df %>% mutate(m = pmap_dbl(list(x, y, z), min))