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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
  • 34.38 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,
  "n_2014_2nd_quarter_apr_june" DOUBLE,
  "n_2014_3rd_quarter_july_sep" DOUBLE,
  "n_2014_4th_quarter_oct_dec" DOUBLE,
  "n_2015_1st_quarter_jan_march" DOUBLE,
  "n_2015_2nd_quarter_apr_june" DOUBLE,
  "n_2015_3rd_quarter_july_sep" DOUBLE,
  "n_2015_4th_quarter_oct_dec" DOUBLE,
  "n_2016_1st_quarter_jan_march" DOUBLE,
  "n_2016_2nd_quarter_apr_june" DOUBLE,
  "n_2016_3rd_quarter_july_sep" DOUBLE,
  "n_2016_4th_quarter_oct_dec" DOUBLE,
  "n_2017_1st_quarter_jan_march" VARCHAR,
  "n_2017_2nd_quarter_apr_june" DOUBLE,
  "n_2017_3rd_quarter_july_sep" DOUBLE,
  "n_2017_4th_quarter_oct_dec" DOUBLE,
  "n_2018_1st_quarter_jan_march" DOUBLE,
  "n_2018_2nd_quarter_apr_june" DOUBLE,
  "n_2018_3rd_quarter_july_sep" DOUBLE,
  "n_2018_4th_quarter_oct_dec" DOUBLE,
  "n_2019_1st_quarter_jan_march" DOUBLE,
  "n_2019_2nd_quarter_apr_june" DOUBLE,
  "n_2019_3rd_quarter_july_sep" DOUBLE,
  "n_2019_4th_quarter_oct_dec" DOUBLE,
  "n_2020_1st_quarter_jan_march" DOUBLE,
  "unnamed_26" VARCHAR,
  "n_2020_2nd_quarter_apr_june" DOUBLE,
  "unnamed_28" VARCHAR,
  "n_2020_3rd_quarter_july_sep" DOUBLE,
  "unnamed_30" VARCHAR,
  "n_2020_4th_quarter_oct_dec" DOUBLE
);

Country Wise Age Group

@kaggle.arnavvvvv_world_tourism.country_wise_age_group
  • 48.61 KB
  • 63 rows
  • 50 columns
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CREATE TABLE country_wise_age_group (
  "country_of_nationality" VARCHAR,
  "n_2014_0_14" DOUBLE,
  "n__2014_15_24" DOUBLE,
  "n__2014_25_34" DOUBLE,
  "n_2014_35_44" DOUBLE,
  "n_2014_45_54" DOUBLE,
  "n_2014_55_64" DOUBLE,
  "n_2014_65_and_above" DOUBLE,
  "n_2015_0_14" DOUBLE,
  "n__2015_15_24" DOUBLE,
  "n__2015_25_34" DOUBLE,
  "n_2015_35_44" DOUBLE,
  "n_2015_45_54" DOUBLE,
  "n_2015_55_64" DOUBLE,
  "n_2015_65_and_above" DOUBLE,
  "n_2016_0_14" DOUBLE,
  "n__2016_15_24" DOUBLE,
  "n__2016_25_34" DOUBLE,
  "n_2016_35_44" DOUBLE,
  "n_2016_45_54" DOUBLE,
  "n_2016_55_64" DOUBLE,
  "n_2016_65_and_above" DOUBLE,
  "n_2017_0_14" DOUBLE,
  "n__2017_15_24" DOUBLE,
  "n__2017_25_34" DOUBLE,
  "n_2017_35_44" DOUBLE,
  "n_2017_45_54" DOUBLE,
  "n_2017_55_64" VARCHAR,
  "n_2017_65_and_above" VARCHAR,
  "n_2018_0_14" DOUBLE,
  "n__2018_15_24" DOUBLE,
  "n__2018_25_34" DOUBLE,
  "n_2018_35_44" DOUBLE,
  "n_2018_45_54" DOUBLE,
  "n_2018_55_64" DOUBLE,
  "n_2018_65_and_above" DOUBLE,
  "n_2019_0_14" DOUBLE,
  "n__2019_15_24" DOUBLE,
  "n__2019_25_34" DOUBLE,
  "n_2019_35_44" DOUBLE,
  "n_2019_45_54" DOUBLE,
  "n_2019_55_64" DOUBLE,
  "n_2019_65_and_above" DOUBLE,
  "n_2020_0_14" DOUBLE,
  "n__2020_15_24" DOUBLE,
  "n__2020_25_34" DOUBLE,
  "n_2020_35_44" DOUBLE,
  "n_2020_45_54" DOUBLE,
  "n_2020_55_64" DOUBLE,
  "n_2020_65_and_above" DOUBLE
);

Country Wise Airport

@kaggle.arnavvvvv_world_tourism.country_wise_airport
  • 58.15 KB
  • 63 rows
  • 57 columns
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CREATE TABLE country_wise_airport (
  "country_of_nationality" VARCHAR,
  "n_2014_delhi_airport" DOUBLE,
  "n__2014_mumbai_airport" DOUBLE,
  "n__2014_chennai_airport" DOUBLE,
  "n_2014_calicut_airport" DOUBLE,
  "n_2014_benguluru_airport" DOUBLE,
  "n_2014_kolkata_airport" DOUBLE,
  "n_2014_hyderabad_airport" DOUBLE,
  "n_2014_cochin_airport" BIGINT,
  "n_2015_delhi_airport" DOUBLE,
  "n__2015_mumbai_airport" DOUBLE,
  "n__2015_chennai_airport" DOUBLE,
  "n_2015_calicut_airport" DOUBLE,
  "n_2015_benguluru_airport" DOUBLE,
  "n_2015_kolkata_airport" DOUBLE,
  "n_2015_hyderabad_airport" DOUBLE,
  "n_2015_cochin_airport" BIGINT,
  "n_2016_delhi_airport" VARCHAR,
  "n__2016_mumbai_airport" VARCHAR,
  "n__2016_chennai_airport" VARCHAR,
  "n_2016_calicut_airport" DOUBLE,
  "n_2016_benguluru_airport" DOUBLE,
  "n_2016_kolkata_airport" VARCHAR,
  "n_2016_hyderabad_airport" VARCHAR,
  "n_2016_cochin_airport" VARCHAR,
  "n_2017_delhi_airport" DOUBLE,
  "n__2017_mumbai_airport" VARCHAR,
  "n__2017_chennai_airport" DOUBLE,
  "n_2017_calicut_airport" DOUBLE,
  "n_2017_benguluru_airport" DOUBLE,
  "n_2017_kolkata_airport" DOUBLE,
  "n_2017_hyderabad_airport" DOUBLE,
  "n_2017_cochin_airport" VARCHAR,
  "n_2018_delhi_airport" DOUBLE,
  "n__2018_mumbai_airport" DOUBLE,
  "n__2018_chennai_airport" DOUBLE,
  "n_2018_calicut_airport" DOUBLE,
  "n_2018_benguluru_airport" DOUBLE,
  "n_2018_kolkata_airport" DOUBLE,
  "n_2018_hyderabad_airport" DOUBLE,
  "n_2018_cochin_airport" DOUBLE,
  "n_2019_delhi_airport" DOUBLE,
  "n__2019_mumbai_airport" DOUBLE,
  "n__2019_chennai_airport" DOUBLE,
  "n_2019_calicut_airport" DOUBLE,
  "n_2019_benguluru_airport" DOUBLE,
  "n_2019_kolkata_airport" DOUBLE,
  "n_2019_hyderabad_airport" DOUBLE,
  "n_2019_cochin_airport" DOUBLE,
  "n_2020_delhi_airport" DOUBLE,
  "n__2020_mumbai_airport" DOUBLE,
  "n__2020_chennai_airport" DOUBLE,
  "n_2020_calicut_airport" DOUBLE,
  "n_2020_benguluru_airport" DOUBLE,
  "n_2020_kolkata_airport" DOUBLE,
  "n_2020_hyderabad_airport" DOUBLE,
  "n_2020_cochin_airport" DOUBLE
);

Country Wise Gender

@kaggle.arnavvvvv_world_tourism.country_wise_gender
  • 16.22 KB
  • 63 rows
  • 15 columns
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CREATE TABLE country_wise_gender (
  "country_of_nationality" VARCHAR,
  "n_2014_male" DOUBLE,
  "n_2014_female" DOUBLE,
  "n_2015_male" DOUBLE,
  "n_2015_female" DOUBLE,
  "n_2016_male" DOUBLE,
  "n_2016_female" DOUBLE,
  "n_2017_male" DOUBLE,
  "n_2017_female" DOUBLE,
  "n_2018_male" DOUBLE,
  "n_2018_female" DOUBLE,
  "n_2019_male" DOUBLE,
  "n_2019_female" DOUBLE,
  "n_2020_male" DOUBLE,
  "n_2020_female" DOUBLE
);

Country Wise Visitors Ways

@kaggle.arnavvvvv_world_tourism.country_wise_visitors_ways
  • 25.04 KB
  • 63 rows
  • 29 columns
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CREATE TABLE country_wise_visitors_ways (
  "country_of_nationality" VARCHAR,
  "n_2014_air" DOUBLE,
  "n_2014_sea" DOUBLE,
  "n_2014_rail" DOUBLE,
  "n_2014_land" DOUBLE,
  "n_2015_air" DOUBLE,
  "n_2015_sea" DOUBLE,
  "n_2015_rail" DOUBLE,
  "n_2015_land" DOUBLE,
  "n_2016_air" DOUBLE,
  "n_2016_sea" DOUBLE,
  "n_2016_rail" DOUBLE,
  "n_2016_land" DOUBLE,
  "n_2017_air" VARCHAR,
  "n_2017_sea" DOUBLE,
  "n_2017_rail" DOUBLE,
  "n_2017_land" DOUBLE,
  "n_2018_air" DOUBLE,
  "n_2018_sea" DOUBLE,
  "n_2018_rail" DOUBLE,
  "n_2018_land" DOUBLE,
  "n_2019_air" DOUBLE,
  "n_2019_sea" DOUBLE,
  "n_2019_rail" DOUBLE,
  "n_2019_land" DOUBLE,
  "n_2020_air" DOUBLE,
  "n_2020_sea" DOUBLE,
  "n_2020_rail" DOUBLE,
  "n_2020_land" DOUBLE
);

Country Wise Yearly Visitors

@kaggle.arnavvvvv_world_tourism.country_wise_yearly_visitors
  • 9.02 KB
  • 63 rows
  • 8 columns
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CREATE TABLE country_wise_yearly_visitors (
  "country" VARCHAR,
  "n_2014" BIGINT,
  "n_2015" BIGINT,
  "n_2016" BIGINT,
  "n_2017" BIGINT,
  "n_2018" BIGINT,
  "n_2019" BIGINT,
  "n_2020" BIGINT
);

General Data 2014–2020

@kaggle.arnavvvvv_world_tourism.general_data_2014_2020
  • 18.73 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
  • 9.49 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
  • 9.58 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
  • 15.02 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
  • 14.47 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
  • 14.8 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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