This notebook will show you how to create and query a table or DataFrame that you uploaded to DBFS. DBFS is a Databricks File System that allows you to store data for querying inside of Databricks. This notebook assumes that you have a file already inside of DBFS that you would like to read from.
This notebook is written in Python so the default cell type is Python. However, you can use different languages by using the %LANGUAGE syntax. Python, Scala, SQL, and R are all supported.
In [2]:
# File location and type
file_location = "/FileStore/tables/covid_19_spread-6c970.csv"
file_type = "csv"
# CSV options
infer_schema = "true"
first_row_is_header = "true"
delimiter = ","
# The applied options are for CSV files. For other file types, these will be ignored.
df = spark.read.format(file_type) \
.option("inferSchema", infer_schema) \
.option("header", first_row_is_header) \
.option("sep", delimiter) \
.load(file_location)
display(df)
country date since_1st_confirmed_n_days since_100th_confirmed_n_days since_1st_recovered_n_days since_100th_recovered_n_days since_1st_deaths_n_days since_10th_deaths_n_days confirmed_n_lag_7d recovered_n_lag_7d deaths_n_lag_7d active_n_lag_7d confirmed_n_lag_8d recovered_n_lag_8d deaths_n_lag_8d active_n_lag_8d confirmed_n_lag_9d recovered_n_lag_9d deaths_n_lag_9d active_n_lag_9d confirmed_n_lag_10d recovered_n_lag_10d deaths_n_lag_10d active_n_lag_10d label Afghanistan 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Albania 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Algeria 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Andorra 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Argentina 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Armenia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Australia 2020-02-01 6 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 Austria 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Azerbaijan 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bangladesh 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Belarus 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Belgium 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bosnia and Herzegovina 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Brazil 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bulgaria 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Burkina Faso 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cameroon 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Canada 2020-02-01 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 Chile 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 China 2020-02-01 10 10 10 4 10 10 1406 39 42 1325 920 36 26 858 643 30 18 595 548 28 17 503 11891 Colombia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cote d'Ivoire 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Croatia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cuba 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Czechia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denmark 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Diamond Princess 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dominican Republic 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ecuador 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Egypt 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Estonia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finland 2020-02-01 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 France 2020-02-01 8 0 0 0 0 0 3 0 0 3 2 0 0 2 0 0 0 0 0 0 0 0 6 Germany 2020-02-01 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 Greece 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hungary 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Iceland 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 India 2020-02-01 2 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denmark 2020-02-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Diamond Princess 2020-02-12 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 175 Dominican Republic 2020-02-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ecuador 2020-02-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Egypt 2020-02-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Estonia 2020-02-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finland 2020-02-12 14 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1
In [3]:
# Create a view or table
temp_table_name = "covid_19_spread"
df.createOrReplaceTempView(temp_table_name)
In [4]:
%sql
select * from `covid_19_spread`
country date since_1st_confirmed_n_days since_100th_confirmed_n_days since_1st_recovered_n_days since_100th_recovered_n_days since_1st_deaths_n_days since_10th_deaths_n_days confirmed_n_lag_7d recovered_n_lag_7d deaths_n_lag_7d active_n_lag_7d confirmed_n_lag_8d recovered_n_lag_8d deaths_n_lag_8d active_n_lag_8d confirmed_n_lag_9d recovered_n_lag_9d deaths_n_lag_9d active_n_lag_9d confirmed_n_lag_10d recovered_n_lag_10d deaths_n_lag_10d active_n_lag_10d label Afghanistan 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Albania 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Algeria 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Andorra 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Argentina 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Armenia 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Australia 2020-02-01 6 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 Austria 2020-02-01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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In [5]:
# With this registered as a temp view, it will only be available to this particular notebook. If you'd like other users to be able to query this table, you can also create a table from the DataFrame.
# Once saved, this table will persist across cluster restarts as well as allow various users across different notebooks to query this data.
# To do so, choose your table name and uncomment the bottom line.
permanent_table_name = "covid19_spread"
df.write.format("parquet").saveAsTable(permanent_table_name)
Content source: dmitrypetukhov/evangelism
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