aglove2189

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

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'''
Command line tool that takes a csv as input and exports 
a statistical summary of the data points in html format.
'''

import pandas as pd
import pandas_profiling
import argparse
import os

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# -*- coding: utf-8 -*-

import os
import re


pat = '\w*(?=\.git$)'

with open('git.sh', mode='w') as f:
    f.write('#!/usr/bin/env bash\n')

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Set-ExecutionPolicy Bypass -Scope Process -Force; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))
choco install chrome
choco install git
choco install cmder
choco install vscode
choco install anaconda3
conda config --add channels conda-forge
conda install black hyperopt lightgbm loguru mlxtend pandas-profiling pyarrow shap tokei
Start-Process "chrome.exe" "https://www.lfd.uci.edu/~gohlke/pythonlibs/#xgboost"

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Keybase proof

I hereby claim:

• I am aglove2189 on github.
• I am aglove2189 (https://keybase.io/aglove2189) on keybase.
• I have a public key ASATX83xHP59rWWRENkl2UKrcDjWeViiLIqlqPQM3QLuugo

To claim this, I am signing this object:

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# -*- coding: utf-8 -*-
import os
import hashlib
import pandas as pd


def read_sql_cache(sql, con, ttl=None, dirname="_cache", **kwargs):
    """
    pd.read_sql but with a time to live (ttl) cache.

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import string
import collections
from typing import List


# https://leetcode.com/problems/rotate-array/
def rotate(nums: List[int], k: int) -> None:
    k = k % len(nums)
    nums[:] = nums[-k:] + nums[:-k]
    return nums

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# data / ml
numpy
pandas
scikit-learn
shap
sqlalchemy
lightgbm

# inspection
tqdm

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import math


def numerize(num, precision=1):
    if num == 0:
        return "0"
    num = float(f"{num:.3g}")
    m = int(math.log10(abs(num)) // 3)
    numf = f"{num / 1000.0**m:.{precision}f}".rstrip("0").rstrip(".")
    return f"{numf}{['', 'K', 'M', 'B', 'T', 'G'][m]}"

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How to Build Resilient Data Products

Every aspect of your product should contribute to one of these 5 principles:

1. Small
2. Fast
3. Reproducible
4. Transparent
5. Frictionless

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