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India Tourism

Dataset of Foreign visitors into INDIA

@kaggle.arnavvvvv_world_tourism

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About this Dataset

India Tourism

This dataset deals with the visitors of foreigners to INDIA.

It includes foreigners (not Indian), overseas Indian, and crew members, except for some of the foreign arrivals who are not
considered tourists (diplomats, soldiers, permanent residents, visiting cohabitation, and residence).

The Indian Government has compiled, analyzed, and provided statistics on foreign tourists visiting Indian
and overseas tourists by type.

The data materials were prepared for the purpose of utilizing them as basic data for establishing tourism policies and
marketing strategies.

I created this dataset by rebuilding the data provided by the Indian Government for easy analysis.

Tables

Country Quater Wise Visitors

@kaggle.arnavvvvv_world_tourism.country_quater_wise_visitors
  • 35.2 kB
  • 63 rows
  • 32 columns
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CREATE TABLE country_quater_wise_visitors (
  "country_of_nationality" VARCHAR,
  "n_2014_1st_quarter_jan_march" DOUBLE  -- 2014–1st Quarter (Jan-March),
  "n_2014_2nd_quarter_apr_june" DOUBLE  -- 2014–2nd Quarter (Apr-June),
  "n_2014_3rd_quarter_july_sep" DOUBLE  -- 2014–3rd Quarter (July-Sep),
  "n_2014_4th_quarter_oct_dec" DOUBLE  -- 2014–4th Quarter (Oct-Dec)),
  "n_2015_1st_quarter_jan_march" DOUBLE  -- 2015–1st Quarter (Jan-March),
  "n_2015_2nd_quarter_apr_june" DOUBLE  -- 2015–2nd Quarter (Apr-June),
  "n_2015_3rd_quarter_july_sep" DOUBLE  -- 2015–3rd Quarter (July-Sep),
  "n_2015_4th_quarter_oct_dec" DOUBLE  -- 2015–4th Quarter (Oct-Dec),
  "n_2016_1st_quarter_jan_march" DOUBLE  -- 2016–1st Quarter (Jan-March),
  "n_2016_2nd_quarter_apr_june" DOUBLE  -- 2016–2nd Quarter (Apr-June),
  "n_2016_3rd_quarter_july_sep" DOUBLE  -- 2016–3rd Quarter (July-Sep),
  "n_2016_4th_quarter_oct_dec" DOUBLE  -- 2016–4th Quarter (Oct-Dec),
  "n_2017_1st_quarter_jan_march" VARCHAR  -- 2017–1st Quarter (Jan-March),
  "n_2017_2nd_quarter_apr_june" DOUBLE  -- 2017–2nd Quarter (Apr-June),
  "n_2017_3rd_quarter_july_sep" DOUBLE  -- 2017–3rd Quarter (July-Sep),
  "n_2017_4th_quarter_oct_dec" DOUBLE  -- 2017–4th Quarter (Oct-Dec),
  "n_2018_1st_quarter_jan_march" DOUBLE  -- 2018–1st Quarter (Jan-March),
  "n_2018_2nd_quarter_apr_june" DOUBLE  -- 2018–2nd Quarter (Apr-June),
  "n_2018_3rd_quarter_july_sep" DOUBLE  -- 2018–3rd Quarter (July-Sep),
  "n_2018_4th_quarter_oct_dec" DOUBLE  -- 2018–4th Quarter (Oct-Dec),
  "n_2019_1st_quarter_jan_march" DOUBLE  -- 2019–1st Quarter (Jan-March),
  "n_2019_2nd_quarter_apr_june" DOUBLE  -- 2019–2nd Quarter (Apr-June),
  "n_2019_3rd_quarter_july_sep" DOUBLE  -- 2019–3rd Quarter (July-Sep),
  "n_2019_4th_quarter_oct_dec" DOUBLE  -- 2019–4th Quarter (Oct-Dec),
  "n_2020_1st_quarter_jan_march" DOUBLE  -- 2020–1st Quarter (Jan-March),
  "unnamed_26" VARCHAR  -- Unnamed: 26,
  "n_2020_2nd_quarter_apr_june" DOUBLE  -- 2020–2nd Quarter (Apr-June),
  "unnamed_28" VARCHAR  -- Unnamed: 28,
  "n_2020_3rd_quarter_july_sep" DOUBLE  -- 2020–3rd Quarter (July-Sep),
  "unnamed_30" VARCHAR  -- Unnamed: 30,
  "n_2020_4th_quarter_oct_dec" DOUBLE  -- 2020–4th Quarter (Oct-Dec)
);

Country Wise Age Group

@kaggle.arnavvvvv_world_tourism.country_wise_age_group
  • 49.78 kB
  • 63 rows
  • 50 columns
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CREATE TABLE country_wise_age_group (
  "country_of_nationality" VARCHAR,
  "n_2014_0_14" DOUBLE  -- 2014–0-14,
  "n__2014_15_24" DOUBLE  -- 2014–15-24,
  "n__2014_25_34" DOUBLE  -- 2014–25-34,
  "n_2014_35_44" DOUBLE  -- 2014–35-44,
  "n_2014_45_54" DOUBLE  -- 2014–45-54,
  "n_2014_55_64" DOUBLE  -- 2014–55-64,
  "n_2014_65_and_above" DOUBLE  -- 2014–65 AND ABOVE,
  "n_2015_0_14" DOUBLE  -- 2015–0-14,
  "n__2015_15_24" DOUBLE  -- 2015–15-24,
  "n__2015_25_34" DOUBLE  -- 2015–25-34,
  "n_2015_35_44" DOUBLE  -- 2015–35-44,
  "n_2015_45_54" DOUBLE  -- 2015–45-54,
  "n_2015_55_64" DOUBLE  -- 2015–55-64,
  "n_2015_65_and_above" DOUBLE  -- 2015–65 AND ABOVE,
  "n_2016_0_14" DOUBLE  -- 2016–0-14,
  "n__2016_15_24" DOUBLE  -- 2016–15-24,
  "n__2016_25_34" DOUBLE  -- 2016–25-34,
  "n_2016_35_44" DOUBLE  -- 2016–35-44,
  "n_2016_45_54" DOUBLE  -- 2016–45-54,
  "n_2016_55_64" DOUBLE  -- 2016–55-64,
  "n_2016_65_and_above" DOUBLE  -- 2016–65 AND ABOVE,
  "n_2017_0_14" DOUBLE  -- 2017–0-14,
  "n__2017_15_24" DOUBLE  -- 2017–15-24,
  "n__2017_25_34" DOUBLE  -- 2017–25-34,
  "n_2017_35_44" DOUBLE  -- 2017–35-44,
  "n_2017_45_54" DOUBLE  -- 2017–45-54,
  "n_2017_55_64" VARCHAR  -- 2017–55-64,
  "n_2017_65_and_above" VARCHAR  -- 2017–65 AND ABOVE,
  "n_2018_0_14" DOUBLE  -- 2018–0-14,
  "n__2018_15_24" DOUBLE  -- 2018–15-24,
  "n__2018_25_34" DOUBLE  -- 2018–25-34,
  "n_2018_35_44" DOUBLE  -- 2018–35-44,
  "n_2018_45_54" DOUBLE  -- 2018–45-54,
  "n_2018_55_64" DOUBLE  -- 2018–55-64,
  "n_2018_65_and_above" DOUBLE  -- 2018–65 AND ABOVE,
  "n_2019_0_14" DOUBLE  -- 2019–0-14,
  "n__2019_15_24" DOUBLE  -- 2019–15-24,
  "n__2019_25_34" DOUBLE  -- 2019–25-34,
  "n_2019_35_44" DOUBLE  -- 2019–35-44,
  "n_2019_45_54" DOUBLE  -- 2019–45-54,
  "n_2019_55_64" DOUBLE  -- 2019–55-64,
  "n_2019_65_and_above" DOUBLE  -- 2019–65 AND ABOVE,
  "n_2020_0_14" DOUBLE  -- 2020–0-14,
  "n__2020_15_24" DOUBLE  -- 2020–15-24,
  "n__2020_25_34" DOUBLE  -- 2020–25-34,
  "n_2020_35_44" DOUBLE  -- 2020–35-44,
  "n_2020_45_54" DOUBLE  -- 2020–45-54,
  "n_2020_55_64" DOUBLE  -- 2020–55-64,
  "n_2020_65_and_above" DOUBLE  -- 2020–65 AND ABOVE
);

Country Wise Airport

@kaggle.arnavvvvv_world_tourism.country_wise_airport
  • 59.54 kB
  • 63 rows
  • 57 columns
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CREATE TABLE country_wise_airport (
  "country_of_nationality" VARCHAR,
  "n_2014_delhi_airport" DOUBLE  -- 2014 Delhi (Airport),
  "n__2014_mumbai_airport" DOUBLE  -- 2014 Mumbai (Airport),
  "n__2014_chennai_airport" DOUBLE  -- 2014 Chennai (Airport),
  "n_2014_calicut_airport" DOUBLE  -- 2014 Calicut (Airport),
  "n_2014_benguluru_airport" DOUBLE  -- 2014 Benguluru (Airport),
  "n_2014_kolkata_airport" DOUBLE  -- 2014 Kolkata (Airport),
  "n_2014_hyderabad_airport" DOUBLE  -- 2014 Hyderabad (Airport),
  "n_2014_cochin_airport" BIGINT  -- 2014 Cochin (Airport),
  "n_2015_delhi_airport" DOUBLE  -- 2015 Delhi (Airport),
  "n__2015_mumbai_airport" DOUBLE  -- 2015 Mumbai (Airport),
  "n__2015_chennai_airport" DOUBLE  -- 2015 Chennai (Airport),
  "n_2015_calicut_airport" DOUBLE  -- 2015 Calicut (Airport),
  "n_2015_benguluru_airport" DOUBLE  -- 2015 Benguluru (Airport),
  "n_2015_kolkata_airport" DOUBLE  -- 2015 Kolkata (Airport),
  "n_2015_hyderabad_airport" DOUBLE  -- 2015 Hyderabad (Airport),
  "n_2015_cochin_airport" BIGINT  -- 2015 Cochin (Airport),
  "n_2016_delhi_airport" VARCHAR  -- 2016 Delhi (Airport),
  "n__2016_mumbai_airport" VARCHAR  -- 2016 Mumbai (Airport),
  "n__2016_chennai_airport" VARCHAR  -- 2016 Chennai (Airport),
  "n_2016_calicut_airport" DOUBLE  -- 2016 Calicut (Airport),
  "n_2016_benguluru_airport" DOUBLE  -- 2016 Benguluru (Airport),
  "n_2016_kolkata_airport" VARCHAR  -- 2016 Kolkata (Airport),
  "n_2016_hyderabad_airport" VARCHAR  -- 2016 Hyderabad (Airport),
  "n_2016_cochin_airport" VARCHAR  -- 2016 Cochin (Airport),
  "n_2017_delhi_airport" DOUBLE  -- 2017 Delhi (Airport),
  "n__2017_mumbai_airport" VARCHAR  -- 2017 Mumbai (Airport),
  "n__2017_chennai_airport" DOUBLE  -- 2017 Chennai (Airport),
  "n_2017_calicut_airport" DOUBLE  -- 2017 Calicut (Airport),
  "n_2017_benguluru_airport" DOUBLE  -- 2017 Benguluru (Airport),
  "n_2017_kolkata_airport" DOUBLE  -- 2017 Kolkata (Airport),
  "n_2017_hyderabad_airport" DOUBLE  -- 2017 Hyderabad (Airport),
  "n_2017_cochin_airport" VARCHAR  -- 2017 Cochin (Airport),
  "n_2018_delhi_airport" DOUBLE  -- 2018 Delhi (Airport),
  "n__2018_mumbai_airport" DOUBLE  -- 2018 Mumbai (Airport),
  "n__2018_chennai_airport" DOUBLE  -- 2018 Chennai (Airport),
  "n_2018_calicut_airport" DOUBLE  -- 2018 Calicut (Airport),
  "n_2018_benguluru_airport" DOUBLE  -- 2018 Benguluru (Airport),
  "n_2018_kolkata_airport" DOUBLE  -- 2018 Kolkata (Airport),
  "n_2018_hyderabad_airport" DOUBLE  -- 2018 Hyderabad (Airport),
  "n_2018_cochin_airport" DOUBLE  -- 2018 Cochin (Airport),
  "n_2019_delhi_airport" DOUBLE  -- 2019 Delhi (Airport),
  "n__2019_mumbai_airport" DOUBLE  -- 2019 Mumbai (Airport),
  "n__2019_chennai_airport" DOUBLE  -- 2019 Chennai (Airport),
  "n_2019_calicut_airport" DOUBLE  -- 2019 Calicut (Airport),
  "n_2019_benguluru_airport" DOUBLE  -- 2019 Benguluru (Airport),
  "n_2019_kolkata_airport" DOUBLE  -- 2019 Kolkata (Airport),
  "n_2019_hyderabad_airport" DOUBLE  -- 2019 Hyderabad (Airport),
  "n_2019_cochin_airport" DOUBLE  -- 2019 Cochin (Airport),
  "n_2020_delhi_airport" DOUBLE  -- 2020 Delhi (Airport),
  "n__2020_mumbai_airport" DOUBLE  -- 2020 Mumbai (Airport),
  "n__2020_chennai_airport" DOUBLE  -- 2020 Chennai (Airport),
  "n_2020_calicut_airport" DOUBLE  -- 2020 Calicut (Airport),
  "n_2020_benguluru_airport" DOUBLE  -- 2020 Benguluru (Airport),
  "n_2020_kolkata_airport" DOUBLE  -- 2020 Kolkata (Airport),
  "n_2020_hyderabad_airport" DOUBLE  -- 2020 Hyderabad (Airport),
  "n_2020_cochin_airport" DOUBLE  -- 2020 Cochin (Airport)
);

Country Wise Gender

@kaggle.arnavvvvv_world_tourism.country_wise_gender
  • 16.61 kB
  • 63 rows
  • 15 columns
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CREATE TABLE country_wise_gender (
  "country_of_nationality" VARCHAR,
  "n_2014_male" DOUBLE  -- 2014 Male,
  "n_2014_female" DOUBLE  -- 2014 Female,
  "n_2015_male" DOUBLE  -- 2015 Male,
  "n_2015_female" DOUBLE  -- 2015 Female,
  "n_2016_male" DOUBLE  -- 2016 Male,
  "n_2016_female" DOUBLE  -- 2016 Female,
  "n_2017_male" DOUBLE  -- 2017 Male,
  "n_2017_female" DOUBLE  -- 2017 Female,
  "n_2018_male" DOUBLE  -- 2018 Male,
  "n_2018_female" DOUBLE  -- 2018 Female,
  "n_2019_male" DOUBLE  -- 2019 Male,
  "n_2019_female" DOUBLE  -- 2019 Female,
  "n_2020_male" DOUBLE  -- 2020 Male,
  "n_2020_female" DOUBLE  -- 2020 Female
);

Country Wise Visitors Ways

@kaggle.arnavvvvv_world_tourism.country_wise_visitors_ways
  • 25.64 kB
  • 63 rows
  • 29 columns
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CREATE TABLE country_wise_visitors_ways (
  "country_of_nationality" VARCHAR,
  "n_2014_air" DOUBLE  -- 2014 AIR,
  "n_2014_sea" DOUBLE  -- 2014 SEA,
  "n_2014_rail" DOUBLE  -- 2014 RAIL,
  "n_2014_land" DOUBLE  -- 2014 LAND,
  "n_2015_air" DOUBLE  -- 2015 AIR,
  "n_2015_sea" DOUBLE  -- 2015 SEA,
  "n_2015_rail" DOUBLE  -- 2015 RAIL,
  "n_2015_land" DOUBLE  -- 2015 LAND,
  "n_2016_air" DOUBLE  -- 2016 AIR,
  "n_2016_sea" DOUBLE  -- 2016 SEA,
  "n_2016_rail" DOUBLE  -- 2016 RAIL,
  "n_2016_land" DOUBLE  -- 2016 LAND,
  "n_2017_air" VARCHAR  -- 2017 AIR,
  "n_2017_sea" DOUBLE  -- 2017 SEA,
  "n_2017_rail" DOUBLE  -- 2017 RAIL,
  "n_2017_land" DOUBLE  -- 2017 LAND,
  "n_2018_air" DOUBLE  -- 2018 AIR,
  "n_2018_sea" DOUBLE  -- 2018 SEA,
  "n_2018_rail" DOUBLE  -- 2018 RAIL,
  "n_2018_land" DOUBLE  -- 2018 LAND,
  "n_2019_air" DOUBLE  -- 2019 AIR,
  "n_2019_sea" DOUBLE  -- 2019 SEA,
  "n_2019_rail" DOUBLE  -- 2019 RAIL,
  "n_2019_land" DOUBLE  -- 2019 LAND,
  "n_2020_air" DOUBLE  -- 2020 AIR,
  "n_2020_sea" DOUBLE  -- 2020 SEA,
  "n_2020_rail" DOUBLE  -- 2020 RAIL,
  "n_2020_land" DOUBLE  -- 2020 LAND
);

Country Wise Yearly Visitors

@kaggle.arnavvvvv_world_tourism.country_wise_yearly_visitors
  • 9.24 kB
  • 63 rows
  • 8 columns
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CREATE TABLE country_wise_yearly_visitors (
  "country" VARCHAR,
  "n_2014" BIGINT  -- 2014,
  "n_2015" BIGINT  -- 2015,
  "n_2016" BIGINT  -- 2016,
  "n_2017" BIGINT  -- 2017,
  "n_2018" BIGINT  -- 2018,
  "n_2019" BIGINT  -- 2019,
  "n_2020" BIGINT  -- 2020
);

General Data 2014–2020

@kaggle.arnavvvvv_world_tourism.general_data_2014_2020
  • 19.18 kB
  • 7 rows
  • 27 columns
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CREATE TABLE general_data_2014_2020 (
  "year" BIGINT,
  "noftaii" DOUBLE,
  "noftaiiagr" DOUBLE,
  "noindfi" DOUBLE,
  "noindfiagr" DOUBLE,
  "nodtvasu" DOUBLE,
  "nodtvasuagr" DOUBLE,
  "feeftit" BIGINT,
  "feeftitagr" DOUBLE,
  "feeftust" DOUBLE,
  "feeftustagr" DOUBLE,
  "wnoita" BIGINT,
  "wnoitaagr" DOUBLE,
  "witr" BIGINT,
  "witragr" DOUBLE,
  "aprnoita" DOUBLE,
  "aprnoitaagr" DOUBLE,
  "apfitr" DOUBLE,
  "apritragr" DOUBLE,
  "ipwiita" DOUBLE,
  "ipwirwta" VARCHAR,
  "ipwsiitr" DOUBLE,
  "ipwirwtr" VARCHAR,
  "ipaprita" DOUBLE,
  "ipaprirta" VARCHAR,
  "ipapritr" DOUBLE,
  "ipaprirtr" VARCHAR
);

Month Wise Ffa

@kaggle.arnavvvvv_world_tourism.month_wise_ffa
  • 9.71 kB
  • 7 rows
  • 13 columns
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CREATE TABLE month_wise_ffa (
  "year" DOUBLE,
  "january" DOUBLE,
  "february" DOUBLE,
  "march" DOUBLE,
  "april" DOUBLE,
  "may" DOUBLE,
  "june" DOUBLE,
  "july" DOUBLE,
  "august" DOUBLE,
  "september" DOUBLE,
  "october" DOUBLE,
  "november" DOUBLE,
  "december" DOUBLE
);

Month Wise Ffe Dollar

@kaggle.arnavvvvv_world_tourism.month_wise_ffe_dollar
  • 9.81 kB
  • 7 rows
  • 13 columns
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CREATE TABLE month_wise_ffe_dollar (
  "year" BIGINT,
  "january" DOUBLE,
  "february" DOUBLE,
  "march" DOUBLE,
  "april" DOUBLE,
  "may" DOUBLE,
  "june" DOUBLE,
  "july" DOUBLE,
  "augest" DOUBLE,
  "september" DOUBLE,
  "october" DOUBLE,
  "november" DOUBLE,
  "december" DOUBLE
);

Top 10 Country Ffa

@kaggle.arnavvvvv_world_tourism.top_10_country_ffa
  • 15.38 kB
  • 7 rows
  • 21 columns
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CREATE TABLE top_10_country_ffa (
  "year" BIGINT,
  "top1_country" VARCHAR,
  "top1_ftas" BIGINT,
  "top2_country" VARCHAR,
  "top2_ftas" BIGINT,
  "top3_country" VARCHAR,
  "top3_ftas" BIGINT,
  "top4_country" VARCHAR,
  "top4_ftas" BIGINT,
  "top5_country" VARCHAR,
  "top5_ftas" BIGINT,
  "top6_country" VARCHAR,
  "top6_ftas" BIGINT,
  "top7_country" VARCHAR,
  "top7_ftas" BIGINT,
  "top8_country" VARCHAR,
  "top8_ftas" BIGINT,
  "top9_country" VARCHAR,
  "top9_ftas" BIGINT,
  "top10_country" VARCHAR,
  "top10_ftas" BIGINT
);

Top10 State Ffa Visit

@kaggle.arnavvvvv_world_tourism.top10_state_ffa_visit
  • 14.82 kB
  • 7 rows
  • 21 columns
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CREATE TABLE top10_state_ffa_visit (
  "year" DOUBLE,
  "top1_state" VARCHAR,
  "top1_ftv" BIGINT,
  "top2_state" VARCHAR,
  "top2_ftv" BIGINT,
  "top3_state" VARCHAR,
  "top3_ftv" BIGINT,
  "top4_state" VARCHAR,
  "top4_ftv" BIGINT,
  "top5_state" VARCHAR,
  "top5_ftv" BIGINT,
  "top6_state" VARCHAR,
  "top6_ftv" BIGINT,
  "top7_state" VARCHAR,
  "top7_ftv" BIGINT,
  "top8_state" VARCHAR,
  "top8_ftv" BIGINT,
  "top9_state" VARCHAR,
  "top9_ftv" BIGINT,
  "top10_state" VARCHAR,
  "top10_ftv" BIGINT
);

Top 10 State Visit

@kaggle.arnavvvvv_world_tourism.top_10_state_visit
  • 15.16 kB
  • 7 rows
  • 21 columns
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CREATE TABLE top_10_state_visit (
  "year" DOUBLE,
  "top1_state" VARCHAR,
  "top1_ftv" BIGINT,
  "top2_state" VARCHAR,
  "top2_ftv" BIGINT,
  "top3_state" VARCHAR,
  "top3_ftv" BIGINT,
  "top4_state" VARCHAR,
  "top4_ftv" BIGINT,
  "top5_state" VARCHAR,
  "top5_ftv" BIGINT,
  "top6_state" VARCHAR,
  "top6_ftv" BIGINT,
  "top7_state" VARCHAR,
  "top7_ftv" BIGINT,
  "top8_state" VARCHAR,
  "top8_ftv" BIGINT,
  "top9_state" VARCHAR,
  "top9_ftv" BIGINT,
  "top10_state" VARCHAR,
  "top10_ftv" BIGINT
);

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