Baselight

Python Data Visualization Essentials Guide

For Exercises in Data Visualization

@kaggle.kalilurrahman_python_data_visualization_essentials_guide

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About this Dataset

Python Data Visualization Essentials Guide

context

The information collated for Python Data Visualization Essentials Guide - Book

sources

Generally available in the public internet

inspiration

All the great data scientists, statisticians, programmers and enthusiasts

availability

Available in a github page https://github.com/kalilurrahman/dataset

Tables

N 1976–2020 President

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_1976_2020_president
  • 64.77 KB
  • 4287 rows
  • 15 columns
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CREATE TABLE n_1976_2020_president (
  "year" BIGINT,
  "state" VARCHAR,
  "state_po" VARCHAR,
  "state_fips" BIGINT,
  "state_cen" BIGINT,
  "state_ic" BIGINT,
  "office" VARCHAR,
  "candidate" VARCHAR,
  "party_detailed" VARCHAR,
  "writein" VARCHAR,
  "candidatevotes" BIGINT,
  "totalvotes" BIGINT,
  "version" BIGINT,
  "notes" VARCHAR,
  "party_simplified" VARCHAR
);

N 19762016usprez

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_19762016usprez
  • 55.67 KB
  • 3740 rows
  • 14 columns
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CREATE TABLE n_19762016usprez (
  "year" BIGINT,
  "state" VARCHAR,
  "state_po" VARCHAR,
  "state_fips" BIGINT,
  "state_cen" BIGINT,
  "state_ic" BIGINT,
  "office" VARCHAR,
  "candidate" VARCHAR,
  "party" VARCHAR,
  "writein" BOOLEAN,
  "candidatevotes" BIGINT,
  "totalvotes" BIGINT,
  "version" BIGINT,
  "notes" VARCHAR
);

N 2015

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_2015
  • 24.12 KB
  • 158 rows
  • 12 columns
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CREATE TABLE n_2015 (
  "country" VARCHAR,
  "region" VARCHAR,
  "happiness_rank" BIGINT,
  "happiness_score" DOUBLE,
  "standard_error" DOUBLE,
  "economy_gdp_per_capita" DOUBLE,
  "family" DOUBLE,
  "health_life_expectancy" DOUBLE,
  "freedom" DOUBLE,
  "trust_government_corruption" DOUBLE,
  "generosity" DOUBLE,
  "dystopia_residual" DOUBLE
);

N 2016

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_2016
  • 25.83 KB
  • 157 rows
  • 13 columns
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CREATE TABLE n_2016 (
  "country" VARCHAR,
  "region" VARCHAR,
  "happiness_rank" BIGINT,
  "happiness_score" DOUBLE,
  "lower_confidence_interval" DOUBLE,
  "upper_confidence_interval" DOUBLE,
  "economy_gdp_per_capita" DOUBLE,
  "family" DOUBLE,
  "health_life_expectancy" DOUBLE,
  "freedom" DOUBLE,
  "trust_government_corruption" DOUBLE,
  "generosity" DOUBLE,
  "dystopia_residual" DOUBLE
);

N 2017

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_2017
  • 24.34 KB
  • 155 rows
  • 12 columns
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CREATE TABLE n_2017 (
  "country" VARCHAR,
  "happiness_rank" BIGINT,
  "happiness_score" DOUBLE,
  "whisker_high" DOUBLE,
  "whisker_low" DOUBLE,
  "economy_gdp_per_capita" DOUBLE,
  "family" DOUBLE,
  "health_life_expectancy" DOUBLE,
  "freedom" DOUBLE,
  "generosity" DOUBLE,
  "trust_government_corruption" DOUBLE,
  "dystopia_residual" DOUBLE
);

N 2018

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_2018
  • 16.63 KB
  • 156 rows
  • 9 columns
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CREATE TABLE n_2018 (
  "overall_rank" BIGINT,
  "country_or_region" VARCHAR,
  "score" DOUBLE,
  "gdp_per_capita" DOUBLE,
  "social_support" DOUBLE,
  "healthy_life_expectancy" DOUBLE,
  "freedom_to_make_life_choices" DOUBLE,
  "generosity" DOUBLE,
  "perceptions_of_corruption" DOUBLE
);

N 2019

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.n_2019
  • 16.49 KB
  • 156 rows
  • 9 columns
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CREATE TABLE n_2019 (
  "overall_rank" BIGINT,
  "country_or_region" VARCHAR,
  "score" DOUBLE,
  "gdp_per_capita" DOUBLE,
  "social_support" DOUBLE,
  "healthy_life_expectancy" DOUBLE,
  "freedom_to_make_life_choices" DOUBLE,
  "generosity" DOUBLE,
  "perceptions_of_corruption" DOUBLE
);

Aapl

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.aapl
  • 17.35 KB
  • 253 rows
  • 7 columns
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CREATE TABLE aapl (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "adj_close" DOUBLE,
  "volume" BIGINT
);

Bad Drivers

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.bad_drivers
  • 10.94 KB
  • 51 rows
  • 8 columns
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CREATE TABLE bad_drivers (
  "state" VARCHAR,
  "number_of_drivers_involved_in_fatal_collisions_per_bil_5c925459" DOUBLE,
  "percentage_of_drivers_involved_in_fatal_collisions_who_c00d2341" BIGINT,
  "percentage_of_drivers_involved_in_fatal_collisions_who_7a6df15b" BIGINT,
  "percentage_of_drivers_involved_in_fatal_collisions_who_b16c0b1b" BIGINT,
  "percentage_of_drivers_involved_in_fatal_collisions_who_74107392" BIGINT,
  "car_insurance_premiums" DOUBLE,
  "losses_incurred_by_insurance_companies_for_collisions__74052d1b" DOUBLE
);

Car Crashes

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.car_crashes
  • 8.73 KB
  • 51 rows
  • 8 columns
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CREATE TABLE car_crashes (
  "total" DOUBLE,
  "speeding" DOUBLE,
  "alcohol" DOUBLE,
  "not_distracted" DOUBLE,
  "no_previous" DOUBLE,
  "ins_premium" DOUBLE,
  "ins_losses" DOUBLE,
  "abbrev" VARCHAR
);

Flights

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.flights
  • 328.17 KB
  • 57859 rows
  • 4 columns
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CREATE TABLE flights (
  "lon_departure" DOUBLE,
  "lat_departure" DOUBLE,
  "lon_arrival" DOUBLE,
  "lat_arrival" DOUBLE
);

Liquizpolldata Sheet1–2

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.liquizpolldata_sheet1_2
  • 8.03 KB
  • 100 rows
  • 7 columns
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CREATE TABLE liquizpolldata_sheet1_2 (
  "quiz_number" BIGINT,
  "total_views" BIGINT,
  "total_responses" BIGINT,
  "right_answers" BIGINT,
  "total_likes" BIGINT,
  "avg_right" VARCHAR,
  "max_right" VARCHAR
);

Nasdaq Bt Summary

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.nasdaq_bt_summary
  • 67.69 KB
  • 102 rows
  • 44 columns
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CREATE TABLE nasdaq_bt_summary (
  "stock" VARCHAR,
  "total_return" DOUBLE,
  "cagr" DOUBLE,
  "max_drawdown" DOUBLE,
  "calmar" DOUBLE,
  "mtd" DOUBLE,
  "three_month" DOUBLE,
  "six_month" DOUBLE,
  "ytd" DOUBLE,
  "one_year" DOUBLE,
  "three_year" DOUBLE,
  "five_year" DOUBLE,
  "ten_year" DOUBLE,
  "incep" DOUBLE,
  "daily_sharpe" DOUBLE,
  "daily_sortino" DOUBLE,
  "daily_mean" DOUBLE,
  "daily_vol" DOUBLE,
  "daily_skew" DOUBLE,
  "daily_kurt" DOUBLE,
  "best_day" DOUBLE,
  "worst_day" DOUBLE,
  "monthly_sharpe" DOUBLE,
  "monthly_sortino" DOUBLE,
  "monthly_mean" DOUBLE,
  "monthly_vol" DOUBLE,
  "monthly_skew" DOUBLE,
  "monthly_kurt" DOUBLE,
  "best_month" DOUBLE,
  "worst_month" DOUBLE,
  "yearly_sharpe" DOUBLE,
  "yearly_sortino" DOUBLE,
  "yearly_mean" DOUBLE,
  "yearly_vol" DOUBLE,
  "yearly_skew" DOUBLE,
  "yearly_kurt" DOUBLE,
  "best_year" DOUBLE,
  "worst_year" DOUBLE,
  "avg_drawdown" DOUBLE,
  "avg_drawdown_days" DOUBLE,
  "avg_up_month" DOUBLE,
  "avg_down_month" DOUBLE,
  "win_year_perc" DOUBLE,
  "twelve_month_win_perc" DOUBLE
);

Oecd Health Stat Cancer Statistics

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.oecd_health_stat_cancer_statistics
  • 25.25 KB
  • 2976 rows
  • 11 columns
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CREATE TABLE oecd_health_stat_cancer_statistics (
  "var" VARCHAR,
  "variable" VARCHAR,
  "unit" VARCHAR,
  "measure" VARCHAR,
  "cou" VARCHAR,
  "country" VARCHAR,
  "yea" BIGINT,
  "year" BIGINT,
  "value" DOUBLE,
  "flag_codes" VARCHAR,
  "flags" VARCHAR
);

Titanic

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.titanic
  • 42.13 KB
  • 891 rows
  • 12 columns
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CREATE TABLE titanic (
  "passengerid" BIGINT,
  "survived" BIGINT,
  "pclass" BIGINT,
  "name" VARCHAR,
  "sex" VARCHAR,
  "age" DOUBLE,
  "sibsp" BIGINT,
  "parch" BIGINT,
  "ticket" VARCHAR,
  "fare" DOUBLE,
  "cabin" VARCHAR,
  "embarked" VARCHAR
);

Travel Times

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.travel_times
  • 16.16 KB
  • 205 rows
  • 13 columns
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CREATE TABLE travel_times (
  "date" TIMESTAMP,
  "starttime" VARCHAR,
  "dayofweek" VARCHAR,
  "goingto" VARCHAR,
  "distance" DOUBLE,
  "maxspeed" DOUBLE,
  "avgspeed" DOUBLE,
  "avgmovingspeed" DOUBLE,
  "fueleconomy" VARCHAR,
  "totaltime" DOUBLE,
  "movingtime" DOUBLE,
  "take407all" VARCHAR,
  "comments" VARCHAR
);

Worldcities

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.worldcities
  • 1.42 MB
  • 26569 rows
  • 11 columns
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CREATE TABLE worldcities (
  "city" VARCHAR,
  "city_ascii" VARCHAR,
  "lat" DOUBLE,
  "lng" DOUBLE,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "admin_name" VARCHAR,
  "capital" VARCHAR,
  "population" DOUBLE,
  "id" BIGINT
);

Worldpopulation

@kaggle.kalilurrahman_python_data_visualization_essentials_guide.worldpopulation
  • 8.12 KB
  • 250 rows
  • 4 columns
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CREATE TABLE worldpopulation (
  "region" VARCHAR,
  "subregion" VARCHAR,
  "key" VARCHAR,
  "value" BIGINT
);

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