aasthavar

This is a collection of books that I've researched, scanned the TOCs of, and am currently working through.  The books are selected based on quality of content, reviews, and reccommendations of various 'best of' lists.

The goal of this collection is to promote mastery of generally applicable programming concepts.

Most topics are covered with Python as the primary language due to its conciseness, which is ideal for learning & practicing new concepts with minimal syntactic boilerplate.

JavaScript & Kotlin are listed in the Tooling section; as they allow extension of VS Code and the IntelliJ suite of IDEs, which cover most development needs.

 

aasthavar

a4b.amazonaws.com
acm-pca.amazonaws.com
acm.amazonaws.com
alexa-appkit.amazon.com
alexa-connectedhome.amazon.com
amazonmq.amazonaws.com
apigateway.amazonaws.com
appflow.amazonaws.com
application-autoscaling.amazonaws.com
appstream.application-autoscaling.amazonaws.com

aasthavar

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

aasthavar

git push origin main
git remote add origin https://github.com/zihangdai/xlnet.git
git archive --format zip --output prototype-code.zip main 

aasthavar

### JupyterNotebooks ###
# gitignore template for Jupyter Notebooks
# website: http://jupyter.org/

.ipynb_checkpoints
*/.ipynb_checkpoints/*

# IPython
profile_default/
ipython_config.py

aasthavar

def missing_data(data):
    total = data.isnull().sum()
    percent = (data.isnull().sum()/data.isnull().count()*100)
    tt = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
    types = []
    for col in data.columns:
        dtype = str(data[col].dtype)
        types.append(dtype)
    tt['Types'] = types
    return(np.transpose(tt))

aasthavar

def unique_values(data):
    total = data.count()
    tt = pd.DataFrame(total)
    tt.columns = ['Total']
    uniques = []
    for col in data.columns:
        unique = data[col].nunique()
        uniques.append(unique)
    tt['Uniques'] = uniques
    return(np.transpose(tt))

aasthavar

# Source: https://www.kaggle.com/getting-started/250322 

1. Identify the problematic data
2. Clean the data
3. Remove, encode, fill in any missing data
4. Remove outliers or analyze them separately
5. Purge contaminated data and correct leaking pipelines
6. Standardize inconsistent data
7. Check if your data makes sense (is valid)
8. Deduplicate multiple records of the same dataForesee and prevent type issues (string issues, DateTime issues)

aasthavar

from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor


def tree_imputation(df):
    missing_cols = [col for col in df.columns if df[col].isnull().sum() > 0]
    non_missing_cols = [col for col in df.columns if df[col].isnull().sum() == 0]
    # num_cols = [col for col in missing_cols if df[col].dtype != 'object']

    # df = df[num_cols]

aasthavar

def likelihood_encoding(df, cat_cols, target_variable = "Status"):
    # cat_cols.remove(target_variable)
    df_temp = df.copy()
    for col in cat_cols:
        effect = {}
        print(col)
        for category in df[col].unique():
            print(category)

            try: