Ejemplos de sql_query en Python

Ejemplo n.º 1

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[2]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[3]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT applications.first_name, applications.last_name, applications.email, purchases.purchase_date
FROM applications
LEFT JOIN purchases
ON applications.first_name = purchases.first_name
AND applications.last_name = purchases.last_name
AND applications.email = purchases.email

Ejemplo n.º 2

Archivo: musclehub.py Proyecto: Okachobi2018/intro-data-capstone-musclehub

# - `visits.gender`
# - `visits.email`
# - `visits.visit_date`
# - `fitness_tests.fitness_test_date`
# - `applications.application_date`
# - `purchases.purchase_date`
#
# Save the result of this query to a variable called `df`.
#
# Hint: your result should have 5004 rows.  Does it?

# In[14]:

df = sql_query(
    '''Select visits.first_name, visits.last_name, visits.gender, visits.visit_date, t2.fitness_test_date, t3.application_date, t4.purchase_date from visits
left join fitness_tests as t2 on visits.first_name = t2.first_name and visits.last_name = t2.last_name and visits.email = t2.email 
left join applications as t3 on visits.first_name = t3.first_name and visits.last_name = t3.last_name and visits.email = t3.email
left join purchases as t4 on visits.first_name = t4.first_name and visits.last_name = t4.last_name and visits.email = t4.email where visit_date >= '7-1-17'
''')
print df

# ## Step 3: Investigate the A and B groups

# We have some data to work with! Import the following modules so that we can start doing analysis:
# - `import pandas as pd`
# - `from matplotlib import pyplot as plt`

# We're going to add some columns to `df` to help us with our analysis.
#
# Start by adding a column called `ab_test_group`.  It should be `A` if `fitness_test_date` is not `None`, and `B` if `fitness_test_date` is `None`.

# In[15]:

Ejemplo n.º 3

Archivo: musclehub.py Proyecto: jpwhelan5/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[5]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[6]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 4

Archivo: musclehub.py Proyecto: spelee/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[36]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[37]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[38]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 5

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[2]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[3]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 6

Archivo: musclehub.py Proyecto: jonnygo55/Capstone---MuscleHub

# Pandas DataFrame.  Here's an example:

# In[53]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import chi2_contingency

# In[54]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[55]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 7

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[2]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[3]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 8

Archivo: musclehub- Richard Lirsac.py Proyecto: LirsacR/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 9

Archivo: musclehub.py Proyecto: pbroer/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[5]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[6]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[7]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 10

# coding: utf-8

# # Capstone Project 1: MuscleHub AB Test

# ## Step 1: Get started with SQL

# In[28]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[31]:

# Examine visits here
sql_query('''
select*
from visits
limit 5''')

# In[32]:

# Examine fitness_tests here
sql_query('''
select*
from fitness_tests
limit 5''')

# In[33]:

# Examine applications here
sql_query('''
select*

Ejemplo n.º 11

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[38]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[18]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 12

Archivo: musclehub.py Proyecto: Kason7/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[3]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[4]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[ ]:


# Here's an example where we save the data to a DataFrame
df1 = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')
print df1

Ejemplo n.º 13

Archivo: musclehub.py Proyecto: jacopom/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
select *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 14

Archivo: musclehub.py Proyecto: Snehulienka88/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[ ]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[ ]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 15

Archivo: musclehub_final.py Proyecto: jtitzer/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[3]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[4]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[5]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# # Let's get started!

Ejemplo n.º 16

# In[3]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[ ]:

import pandas as pd

# In[ ]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5;
''')

# In[ ]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 17

Archivo: musclehub.py Proyecto: matoyajones/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[15]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[2]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[16]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 18

Archivo: script.py Proyecto: zaydalameddine/MuscleHub-A-B-Testing

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 11:24:03 2020

@author: Zayd Alameddine
"""

from codecademySQL import sql_query

df = sql_query(''' 
               SELECT *
               FROM visits
               LIMIT 5''')

print(df.head())

Ejemplo n.º 19

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 20

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')
print df

# ## Step 2: Get your dataset

Ejemplo n.º 21

Archivo: musclehub.py Proyecto: learner-for-life/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[2]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[3]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 22

Archivo: musclehub pedro.py Proyecto: pedroaraujo1/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[255]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[256]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[257]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 23

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
# 
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[4]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[5]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[6]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')


# ## Step 2: Get your dataset

Ejemplo n.º 24

Archivo: musclehub.py Proyecto: katestasiuk/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 25

Archivo: musclehub-ioglesby.py Proyecto: ioglesby/intro-data-capstone-musclehub

# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[83]:


# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query


# In[84]:


# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


# In[85]:


# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

Ejemplo n.º 26

Archivo: musclehub.py Proyecto: schardt8237/Codecademy

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[2]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 27

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[1]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[5]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[3]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 28

Archivo: notebook.py Proyecto: bntky/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[107]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[108]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[109]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')
df

# ## Step 2: Get your dataset

Ejemplo n.º 29

Archivo: musclehub.py Proyecto: aliciagilbert/intro-data-capstone-musclehub

# Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.
#
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[10]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query

# In[11]:

# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')

# In[12]:

# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')

# ## Step 2: Get your dataset

# Let's get started!

Ejemplo n.º 30

Archivo: musclehub.py Proyecto: kaumaron/musclehub_submission

# 
# For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:

# In[ ]:

# This import only needs to happen once, at the beginning of the notebook
from codecademySQL import sql_query
import numpy as np


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# Here's an example of a query that just displays some data
sql_query('''
SELECT *
FROM visits
LIMIT 5
''')


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# Here's an example where we save the data to a DataFrame
df = sql_query('''
SELECT *
FROM applications
LIMIT 5
''')
df.head()