Index

Symbols

!= see logical operator # see comment & see logical operator . see chaining see path .. see path : see column range < see logical operator

<= see logical operator = see assignment symbol == see logical operator > see logical operator >= see logical operator [] see DataFrame | see logical operator

_

__doc__ see documentation

A

accuracy assessment adding columns Airbnb altair, [1], [2] + :N, [1] alt.Legend alt.Scale alt.Tooltip alt.X, [1], [2] alt.Y, [1], [2] configure_axis, [1] count encoding channel, [1] facet graphical mark histogram layers logarithmic scaling mark_bar, [1], [2] mark_circle, [1], [2] mark_line mark_point mark_rule maxbins multiline labels sort, [1] tick count tick formatting title

API, [1] endpoint HTTP query parameters token application programming interface see API argument assign assignment symbol auditable

B

balance BeautifulSoup bitmap see raster graphics bool see data types

bootstrap distribution in Python breast cancer, [1]

C

Canadian languages, [1], [2] canlang data cascading style sheet see CSS categorical variable, [1] causal question definition centering chaining class classification, [1], [2] binary comparison to regression majority overview clustering overview color blindness simulator color palette column assignment column range, [1]

comma-separated values see csv comment compile see ibis concat confidence interval, [1] confusion matrix container count see altair see ibis Craiglist cross-validation, [1], [2] cross_validate folds GridSearchCV crosstab, [1] CSS selector csv, [1]

D

data frame definition, [1] overview see DataFrame data science definition good practices data structures dictionary (dict) list set tuple data types boolean (bool) floating point number (float) integer (int) NoneType (none) string (str) database connection filter rows ordering PostgreSQL reasons to use see ibis select columns SQLite table DataFrame [], [1], [2], [3], [4], [5], [6], [7] abs agg apply astype column assignment groupby, [1], [2], [3] head iloc[] info, [1], [2] loc[], [1], [2], [3], [4] melt merge, [1] nlargest nsmallest, [1] pivot quantile rename, [1] reset_index, [1], [2] sample, [1], [2], [3], [4] sample (bootstrap) sort_values tail to_csv value_counts, [1], [2]

dates and times delimiter see separator descriptive question definition dict see data structures distance K-means K-nearest neighbors more than two variables distribution, [1] Docker image installation tag documentation

E

elbow method, [1] escape character estimation overview

Excel spreadsheet reading exploratory question definition extrapolation

F

false negative false positive feature see predictor feature engineering see predictor design

filtering rows fit see scikit-learn float see data types function

G

git add, [1] clone, [1] commit, [1], [2] installation Jupyter extension merge conflict pull, [1] push, [1]

GitHub, [1] add file collaborator access commit issues pen tool personal access token golden rule of machine learning

H

hash help see documentation HTML selector tag

hypertext markup language see HTML

I

ibis [], [1] compile connect, [1] count execute head, [1] list_tables, [1] order_by postgres see database sqlite table, [1]

imbalance import inference inferential question definition int see data types integer interval see confidence interval irrelevant predictors isin see logical operator Island landmasses

J

JavaScript Object Notation see JSON JSON Jupyter notebook best practices cell execution code cell export kernel markdown cell

JupyterHub JupyterLab Desktop

K

K-means, [1], [2] algorithm restart standardization termination K-nearest neighbors, [1] classification, [1] multivariable regression regression

kernel interrupt restart KMeans inertia_, [1] labels_ n_clusters n_init see scikit-learn

L

line see straight line list comprehension loading see reading loc[] see DataFrame location see path logarithmic scale

logical operator and (&) containment (isin) equivalency (==), [1] greater than (> and >=) inequivalency (!=) less than (< and <=) or (|) logical statement, [1] query see logical operator

M

make_column_transformer see scikit-learn make_pipeline see scikit-learn markdown, [1] Mauna Loa, [1] mean_squared_error see scikit-learn mechanistic question definition

Michelson speed of light, [1] Microsoft Excel see Excel spreadsheet missing data, [1] dropna mean imputation modifying columns multi-line expression multicollinearity multivariable linear equation see plane equation

N

NaN see missing data NASA negative label

nominal NoneType see data types nsmallest numerical variable

O

object naming convention observation, [1] Old Faithful outliers

overfitting classification regression overplotting oversampling

P

package Palmer penguins pandas, [1], [2] parameter ParserError path absolute current local previous relative remote PDF percentile Pipeline see scikit-learn plane equation plot axis labels labels see visualization

population distribution parameter, [1] portable document format see PDF positive label precision assessment predict see scikit-learn prediction accuracy see accuracy predictive question, [1] definition predictor design predictor selection see variable selection

Q

question classification data analysis regression, [1] visualization, [1], [2], [3], [4], [5]

R

random random seed see seed RandomState see seed raster graphics file types read function header argument names argument read_csv, [1], [2], [3] read_excel read_html sep argument skiprows argument reading definition separator, [1] recall assessment regression comparison of methods comparison to classification linear logistic multivariable linear multivariable linear equation overview

relationship linear negative none nonlinear positive strong weak repository local private public remote, [1] see version control reproducible, [1], [2] requests, [1] get json response variable RMSPE, [1], [2], [3] comparison with RMSE root mean square prediction error see RMSPE

S

Sacramento real estate, [1], [2], [3] sample, [1] estimate sampling distribution, [1] compared to bootstrap distribution compared to population distribution effect of sample size shape, [1] scaling scikit-learn, [1], [2], [3] cross_validate fit, [1], [2], [3], [4] GridSearchCV, [1] KMeans, [1] KNeighborsClassifier make_column_selector make_column_transformer, [1], [2], [3] make_pipeline, [1], [2], [3] mean_squared_error model object Pipeline, [1], [2], [3], [4], [5] precision_score, [1] predict, [1], [2], [3] predictors RandomizedSearchCV recall_score, [1] response score, [1] SimpleImputer StandardScaler, [1], [2], [3] train_test_split transform seed numpy.random.seed, [1], [2], [3], [4] selecting columns, [1], [2] sem see standard error semisupervised separator, [1]

Series astype max mean, [1] median min replace size std str.contains str.split str.startswith sum unique value_counts, [1] SettingWithCopyWarning shuffling staging area see git standard error standardization K-means K-nearest neighbors StandardScaler see scikit-learn statistical inference see inference str see data types straight line distance equation stratification string, [1] summarization overview summarize summary statistic supervised

T

tab-separated values see tsv table tabular data test set, [1] tidy data arguments for definition

to_list training set, [1], [2] true negative true positive tsv tuning parameter see parameter type

U

underfitting classification regression, [1]

unsupervised URL reading from

V

validation set value variable, [1] variable selection best subset elbow method forward implementation vector graphics file types

version control repository hosting system visualization bar, [1] explanation histogram line overview scatter, [1], [2], [3] see altair

W

web scraping, [1] permission Wikipedia within-cluster sum of squared distances see WSSD

working directory write function to_csv WSSD see KMeans total, [1]

X

xlsx see Excel spreadsheet