def initialise():
"""
initialise the db
:return: sql_imports
"""
births = load_births()
return births
from sklearn.datasets import load_iris
import pandas as pd
from pandasql import sqldf
from pandasql import load_meat, load_births
import re
births = load_births()
meat = load_meat()
iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
iris_df.columns = [re.sub("[() ]", "", col) for col in iris_df.columns]
dataset = pd.read_csv('c:/temp/data-small.txt', sep='\t')
print(sqldf("SELECT * FROM iris_df LIMIT 10;", locals()))
print(sqldf("SELECT sepalwidthcm, species FROM iris_df LIMIT 10;", locals()))
print(sqldf("SELECT * FROM dataset LIMIT 10;", locals()))
__FILENAME__ = demo
from sklearn.datasets import load_iris
import pandas as pd
from pandasql import sqldf
from pandasql import load_meat, load_births
import re
births = load_births()
meat = load_meat()
iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df["species"] = pd.Categorical(iris.target, levels=iris.target_names)
iris_df.columns = [re.sub("[() ]", "", col) for col in iris_df.columns]
print sqldf("select * from iris_df limit 10;", locals())
print sqldf("select sepalwidthcm, species from iris_df limit 10;", locals())
q = """
select
species
, avg(sepalwidthcm)
, min(sepalwidthcm)
, max(sepalwidthcm)
from
iris_df
group by
species;
"""
print "*" * 80