We had to include the keyword DISTINCT because a single candidate can apply for more than one degree course, so if a candidate made two payments for two courses on time, that candidate would be counted twice (the candidate can be still counted twice in two different columns if the candidate paid for one degree course and didn't pay for another, but that's a different story). It is also possible to apply the COUNT() operator to a DISTINCT SELECT to get a count of distinct values, for instance: SELECT COUNT(DISTINCT. Now, we count the number of candidates who were accepted for at least one degree course, the number of candidates who were conditionally accepted for at least one degree course and not accepted for at least one degree course. We previously counted the number of accepted payments, conditionally accepted payments and not accepted payments. Must use column names rather than with COUNT(DISTINCT. Second, we now count student_ids and not pay_dates. Combine COUNT and DISTINCT to find the number of unique values. First of all, we included the keyword DISTINCT in each COUNT. For example, change the directory to your database location and enter sqlite3 eco.Good! Now, in certain situations you may also want to include DISTINCT in your CASE WHEN statements with COUNT. Filter rows with WHERE Filter multiple values in a column with WHERE and IN Find the unique values in a column with DISTINCT Query value counts with. connect( './eco.db') cur = con.cursor() # Queries cur.execute( "SELECT ROUND(weight/1000,2) FROM survey LIMIT 2 ") # change weight to kg print(cur.fetchall()) # cur.execute( """ SELECT ROUND(weight/1000,2) FROM survey WHERE (year > 2000) AND (species_id IN ('DM','DS','DO')) AND (weight > 1) LIMIT 2 """) print(cur.fetchall()) # cur.execute( """ SELECT species_id, sex, count(*) FROM survey WHERE species_id IN ('DS','DO') GROUP BY sex, species_id ORDER BY count(*) DESC """) print(cur.fetchall()) # cur.execute( """ SELECT species_id, sex, count(*) FROM survey GROUP BY sex, species_id HAVING species_id IN ('DS','DO') ORDER BY count(*) DESC """) print(cur.fetchall()) # cur.execute( """ SELECT species_id, sex, count(*) FROM survey WHERE species_id LIKE 'D%' GROUP BY sex, species_id ORDER BY count(*) DESC """) print(cur.fetchall()) # cur.execute( """ SELECT * FROM survey JOIN species ON species.species_id = survey.species_id WHERE weight '||Īlso, we can use SQLite3 directly from command line for getting queries. You can define a UNIQUE constraint at the column or the table level. To define a UNIQUE constraint, you use the UNIQUE keyword followed by one or more columns. Import sqlite3 # Connect and read the db con = sqlite3. A UNIQUE constraint ensures all values in a column or a group of columns are distinct from one another or unique. Pandas inserts with ifexists'append' argument so that it keeps the structure defined in SQL Alchemy. Define an ORM structure for the iris dataset, then use pandas to insert the data into an SQLite database. The following are more examples of different SQL queries in Python: Reproducible example using pandas to collect the unique values. connect( './eco.db') cur = con.cursor() # SQL query cur.execute( "SELECT name FROM PRAGMA_TABLE_INFO('survey') ") # select fields' name # Store output out = cur.fetchall() name = for x in out] print(name) # Exe query, store output and close the connection cur.execute( 'SELECT * FROM survey LIMIT 2 ') # select first two rows out = cur.fetchall() con.close() print(out) # Output as a list of dictionaries d = for i in out: d.append( dict( zip(name,i))) print(d) # Import sqlite3 # Connect and read the db con = sqlite3. Now, we can use execute command for running SQL commands in Python. Let’s use the above function to convert “survey.csv” and “species.csv” to tables in “eco.db”. split( ',')) for i in range( len(ro))] zip_ = list( zip(hd, list(fields_type))) hd_fl = header_field = ','.join(hd_fl) header = ','.join(hd) cur.execute( "CREATE TABLE IF NOT EXISTS %s ( %s ) " % (table_name,header_field)) cur.executemany( "INSERT INTO %s ( %s ) VALUES ( %s ) " % (table_name,header,( '?,' * len(hd))), db) con.commit() con.close() if _name_ = '_main_': # Example fields_type_1 = + * 6 + * 2 fields_type_2 = + * 3 csv_sql( './surveys.csv', 'survey', 'eco.db',fields_type_1) csv_sql( './species.csv', 'species', 'eco.db',fields_type_2) Queries connect(database_name) cur = con.cursor() # Drop the current table before re-create: cur.execute( "DROP TABLE IF EXISTS %s " % table_name) with open(file_dir, 'r') as fl: hd = fl.readline().split( ',') ro = fl.readlines() db =. If you are ok with python, > import sqlite3 > c nnect ('mydb.db') > cur c. #!/usr/bin/python3 import sqlite3 def csv_sql(file_dir,table_name,database_name,fields_type): con = sqlite3.
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