Class 13: Data from multiple sources and Databases¶
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import pandas as pd
Merging review¶
df18_players = pd.read_csv('data/2018-players.csv')
df19_players = pd.read_csv('data/2019-players.csv')
df18_players.shape, df19_players.shape
((748, 5), (626, 5))
pd.merge(df18_players,df19_players,how='inner',on='PLAYER_ID')
| Unnamed: 0_x | PLAYER_NAME_x | TEAM_ID_x | PLAYER_ID | SEASON_x | Unnamed: 0_y | PLAYER_NAME_y | TEAM_ID_y | SEASON_y | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 626 | Kawhi Leonard | 1610612761 | 202695 | 2018 | 299 | Kawhi Leonard | 1610612746 | 2019 |
| 1 | 627 | Pascal Siakam | 1610612761 | 1627783 | 2018 | 275 | Pascal Siakam | 1610612761 | 2019 |
| 2 | 628 | Marc Gasol | 1610612761 | 201188 | 2018 | 276 | Marc Gasol | 1610612761 | 2019 |
| 3 | 1157 | Marc Gasol | 1610612763 | 201188 | 2018 | 276 | Marc Gasol | 1610612761 | 2019 |
| 4 | 629 | Danny Green | 1610612761 | 201980 | 2018 | 202 | Danny Green | 1610612747 | 2019 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 533 | 1339 | Abdul Gaddy | 1610612760 | 203583 | 2018 | 561 | Abdul Gaddy | 1610612760 | 2019 |
| 534 | 1342 | Andre Roberson | 1610612760 | 203460 | 2018 | 566 | Andre Roberson | 1610612760 | 2019 |
| 535 | 1348 | Norvel Pelle | 1610612755 | 203658 | 2018 | 24 | Norvel Pelle | 1610612755 | 2019 |
| 536 | 1350 | Denzel Valentine | 1610612741 | 1627756 | 2018 | 58 | Denzel Valentine | 1610612741 | 2019 |
| 537 | 1353 | C.J. Wilcox | 1610612754 | 203912 | 2018 | 573 | C.J. Wilcox | 1610612754 | 2019 |
538 rows × 9 columns
pd.merge(df18_players,df19_players,how='outer',on='PLAYER_ID')
| Unnamed: 0_x | PLAYER_NAME_x | TEAM_ID_x | PLAYER_ID | SEASON_x | Unnamed: 0_y | PLAYER_NAME_y | TEAM_ID_y | SEASON_y | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 626.0 | Kawhi Leonard | 1.610613e+09 | 202695 | 2018.0 | 299.0 | Kawhi Leonard | 1.610613e+09 | 2019.0 |
| 1 | 627.0 | Pascal Siakam | 1.610613e+09 | 1627783 | 2018.0 | 275.0 | Pascal Siakam | 1.610613e+09 | 2019.0 |
| 2 | 628.0 | Marc Gasol | 1.610613e+09 | 201188 | 2018.0 | 276.0 | Marc Gasol | 1.610613e+09 | 2019.0 |
| 3 | 1157.0 | Marc Gasol | 1.610613e+09 | 201188 | 2018.0 | 276.0 | Marc Gasol | 1.610613e+09 | 2019.0 |
| 4 | 629.0 | Danny Green | 1.610613e+09 | 201980 | 2018.0 | 202.0 | Danny Green | 1.610613e+09 | 2019.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 922 | NaN | NaN | NaN | 1629097 | NaN | 619.0 | Terry Larrier | 1.610613e+09 | 2019.0 |
| 923 | NaN | NaN | NaN | 203461 | NaN | 621.0 | Anthony Bennett | 1.610613e+09 | 2019.0 |
| 924 | NaN | NaN | NaN | 203906 | NaN | 623.0 | Devyn Marble | 1.610613e+09 | 2019.0 |
| 925 | NaN | NaN | NaN | 1629755 | NaN | 624.0 | Hassani Gravett | 1.610613e+09 | 2019.0 |
| 926 | NaN | NaN | NaN | 1629721 | NaN | 625.0 | JaKeenan Gant | 1.610613e+09 | 2019.0 |
927 rows × 9 columns
pd.merge(df18_players,df19_players,how='left',on='PLAYER_ID')
| Unnamed: 0_x | PLAYER_NAME_x | TEAM_ID_x | PLAYER_ID | SEASON_x | Unnamed: 0_y | PLAYER_NAME_y | TEAM_ID_y | SEASON_y | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 626 | Kawhi Leonard | 1610612761 | 202695 | 2018 | 299.0 | Kawhi Leonard | 1.610613e+09 | 2019.0 |
| 1 | 627 | Pascal Siakam | 1610612761 | 1627783 | 2018 | 275.0 | Pascal Siakam | 1.610613e+09 | 2019.0 |
| 2 | 628 | Marc Gasol | 1610612761 | 201188 | 2018 | 276.0 | Marc Gasol | 1.610613e+09 | 2019.0 |
| 3 | 629 | Danny Green | 1610612761 | 201980 | 2018 | 202.0 | Danny Green | 1.610613e+09 | 2019.0 |
| 4 | 630 | Kyle Lowry | 1610612761 | 200768 | 2018 | 451.0 | Kyle Lowry | 1.610613e+09 | 2019.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 749 | 1369 | Tyrius Walker | 1610612752 | 1629246 | 2018 | NaN | NaN | NaN | NaN |
| 750 | 1370 | Marcus Lee | 1610612748 | 1629159 | 2018 | NaN | NaN | NaN | NaN |
| 751 | 1371 | Trey Lewis | 1610612762 | 1629163 | 2018 | NaN | NaN | NaN | NaN |
| 752 | 1372 | Emanuel Terry | 1610612743 | 1629150 | 2018 | NaN | NaN | NaN | NaN |
| 753 | 1373 | Justin Bibbs | 1610612738 | 1629167 | 2018 | NaN | NaN | NaN | NaN |
754 rows × 9 columns
df18_games = pd.read_csv('data/2018-games.csv')
df19_games = pd.read_csv('data/2019-games.csv')
games_df = pd.concat([df18_games,df19_games])
games_df.head()
| Unnamed: 0 | GAME_DATE_EST | GAME_ID | GAME_STATUS_TEXT | HOME_TEAM_ID | VISITOR_TEAM_ID | SEASON | TEAM_ID_home | PTS_home | FG_PCT_home | ... | AST_home | REB_home | TEAM_ID_away | PTS_away | FG_PCT_away | FT_PCT_away | FG3_PCT_away | AST_away | REB_away | HOME_TEAM_WINS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16196 | 2019-06-13 | 41800406 | Final | 1610612744 | 1610612761 | 2018 | 1610612744 | 110.0 | 0.488 | ... | 28.0 | 42.0 | 1610612761 | 114.0 | 0.476 | 0.793 | 0.394 | 25.0 | 39.0 | 0 |
| 1 | 16197 | 2019-06-10 | 41800405 | Final | 1610612761 | 1610612744 | 2018 | 1610612761 | 105.0 | 0.447 | ... | 19.0 | 43.0 | 1610612744 | 106.0 | 0.463 | 0.714 | 0.476 | 27.0 | 37.0 | 0 |
| 2 | 16198 | 2019-06-07 | 41800404 | Final | 1610612744 | 1610612761 | 2018 | 1610612744 | 92.0 | 0.449 | ... | 26.0 | 42.0 | 1610612761 | 105.0 | 0.419 | 0.958 | 0.313 | 22.0 | 39.0 | 0 |
| 3 | 16199 | 2019-06-05 | 41800403 | Final | 1610612744 | 1610612761 | 2018 | 1610612744 | 109.0 | 0.396 | ... | 25.0 | 41.0 | 1610612761 | 123.0 | 0.524 | 0.952 | 0.447 | 30.0 | 40.0 | 0 |
| 4 | 16200 | 2019-06-02 | 41800402 | Final | 1610612761 | 1610612744 | 2018 | 1610612761 | 104.0 | 0.372 | ... | 17.0 | 49.0 | 1610612744 | 109.0 | 0.463 | 0.870 | 0.382 | 34.0 | 42.0 | 0 |
5 rows × 22 columns
Data¶
In order to work with large dataset, one option is to use a computer that has more memory, such as an HPC system. An easier option is to load data from a database instead of loading all data into memory. One way of working with databases is to use a Python library named sqlite3 which allows us to create a connection between the database and Python so that we can pull data into Python to work with it.
Warning
To work with a database it needs to be downloaded, we can’t load it via url like we did with the .csv files that we’ve been using, because when we need to make a connection to the database that can dynamically interact with the database.
Today, we’ll work with a database version of the same SAFI dataset we’ve been working with. Download it and put it in a data folder that’s in the same folder as your notebook.
First we’ll import the needed library, this one doesn’t have a standard alias, so we’ll use it by referring to the whole name.
import sqlite3
First we’ll connect to the database
con = sqlite3.connect('data/SQL_SAFI.sqlite')
Next we establish a cursor with the connection and then run a query.
cur = con.cursor()
cur.execute("SELECT * FROM Farms")
<sqlite3.Cursor at 0x7f4994c69180>
Here, “SELECT * FROM Farms” is a query we made into the cursor. It does not return the data, it just says what data to get ready, this is another opportunity to get the data in smaller chunks, we can choose in the next step to bring all or only some of the data from the query’s result into Python.
This query says to get all (*) the data from the Farms table.
Now, we can use this cursor to pull all of the results from that query into our notebook.
rows = cur.fetchall()
rows
[(1,
'Moz',
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-19.11222901,
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(5,
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"['Sept', 'Oct']",
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-19.11229465,
33.48341504,
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13.0,
'uuid:e6ee6269-b467-4e37-91fc-5e9eaf934557'),
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13,
'2017-04-03T03:58:43.000Z',
'2017-04-03T04:19:36.000Z',
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'Bandula',
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'no',
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"['poultry']",
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"['radio']",
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"['Jan', 'Feb', 'Mar', 'Dec']",
"['rely_less_food', 'limit_variety', 'borrow_food']",
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-19.11192854,
33.476780600000005,
696.0,
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'uuid:02cde43c-adcf-4b1d-8ef7-bd665ccab756')]
We can see it returns the data without much structure
type(rows)
list
and examine the type of one element of it as well.
type(rows[0])
tuple
we can also combine use of this library (to access databases) with Pandas functions to pull data from databases directly into a datagrame.
df = pd.read_sql_query("SELECT * FROM Farms",con)
df.head()
| Id | Country | A01_interview_date | A03_quest_no | A04_start | A05_end | A06_province | A07_district | A08_ward | A09_village | ... | F13_du_look_aftr_cows | F14_items_owned | G01_no_meals | G02_months_lack_food | G03_no_food_mitigation | gps:Latitude | gps:Longitude | gps:Altitude | gps:Accuracy | instanceID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Moz | 17/11/2016 | 1 | 2017-03-23T09:49:57.000Z | 2017-04-02T17:29:08.000Z | Manica | Manica | Bandula | God | ... | no | ['bicycle', 'television', 'solar_panel', 'table'] | 2 | ['Jan'] | ['na', 'rely_less_food', 'reduce_meals', 'day_... | -19.112259 | 33.483456 | 698.0 | 14.0 | uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef |
| 1 | 2 | Moz | 17/11/2016 | 1 | 2017-04-02T09:48:16.000Z | 2017-04-02T17:26:19.000Z | Manica | Manica | Bandula | God | ... | no | ['cow_cart', 'bicycle', 'radio', 'cow_plough',... | 2 | ['Jan', 'Sept', 'Oct', 'Nov', 'Dec'] | ['na', 'reduce_meals', 'restrict_adults', 'bor... | -19.112477 | 33.483416 | 690.0 | 19.0 | uuid:099de9c9-3e5e-427b-8452-26250e840d6e |
| 2 | 3 | Moz | 17/11/2016 | 3 | 2017-04-02T14:35:26.000Z | 2017-04-02T17:26:53.000Z | Manica | Manica | Bandula | God | ... | no | ['solar_torch'] | 2 | ['Jan', 'Feb', 'Mar', 'Oct', 'Nov', 'Dec'] | ['na', 'restrict_adults', 'lab_ex_food'] | -19.112108 | 33.483450 | 674.0 | 13.0 | uuid:193d7daf-9582-409b-bf09-027dd36f9007 |
| 3 | 4 | Moz | 17/11/2016 | 4 | 2017-04-02T14:55:18.000Z | 2017-04-02T17:27:16.000Z | Manica | Manica | Bandula | God | ... | no | ['bicycle', 'radio', 'cow_plough', 'solar_pane... | 2 | ['Sept', 'Oct', 'Nov', 'Dec'] | ['na', 'reduce_meals', 'restrict_adults', 'lab... | -19.112229 | 33.483424 | 679.0 | 5.0 | uuid:148d1105-778a-4755-aa71-281eadd4a973 |
| 4 | 5 | Moz | 17/11/2016 | 5 | 2017-04-02T15:10:35.000Z | 2017-04-02T17:27:35.000Z | Manica | Manica | Bandula | God | ... | no | ['motorcyle', 'radio', 'cow_plough', 'mobile_p... | 2 | ['Aug', 'Sept', 'Oct', 'Nov'] | ['na', 'go_forest', 'migrate'] | -19.112217 | 33.483425 | 689.0 | 10.0 | uuid:2c867811-9696-4966-9866-f35c3e97d02d |
5 rows × 61 columns
This is the familiar SAFI data that we’ve been working with
More Specific Queries¶
There are several ways that we can make our queries return only subsets of the data, this is where the database can start to provide real advantages, we can do sophisticated subsetting of our dataset in the database, which is optimized for performance at exactly these tasks, instead of in Python which is a general purpose programming language.
Strucutred Query Langauge (SQL) is a powerful way of working with databases. We’ll use it only through pandas going forward so that we can also use the nice visualizations from the notebook, but there are many other ways to use SQL.
For example we might only be interested in the location of the farms that were surveyed, so we can choose only those columns, by putting the column names in place of the * in the query.
location_df = pd.read_sql_query("SELECT Country, A06_Province, A07_district, A08_ward, A09_village FROM Farms",con)
location_df.head()
| Country | A06_province | A07_district | A08_ward | A09_village | |
|---|---|---|---|---|---|
| 0 | Moz | Manica | Manica | Bandula | God |
| 1 | Moz | Manica | Manica | Bandula | God |
| 2 | Moz | Manica | Manica | Bandula | God |
| 3 | Moz | Manica | Manica | Bandula | God |
| 4 | Moz | Manica | Manica | Bandula | God |
We can limit for the number of rows we get by using “LIMIT” keyword in the query.
pd.read_sql_query("SELECT * FROM Farms LIMIT 10",con)
| Id | Country | A01_interview_date | A03_quest_no | A04_start | A05_end | A06_province | A07_district | A08_ward | A09_village | ... | F13_du_look_aftr_cows | F14_items_owned | G01_no_meals | G02_months_lack_food | G03_no_food_mitigation | gps:Latitude | gps:Longitude | gps:Altitude | gps:Accuracy | instanceID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Moz | 17/11/2016 | 1 | 2017-03-23T09:49:57.000Z | 2017-04-02T17:29:08.000Z | Manica | Manica | Bandula | God | ... | no | ['bicycle', 'television', 'solar_panel', 'table'] | 2 | ['Jan'] | ['na', 'rely_less_food', 'reduce_meals', 'day_... | -19.112259 | 33.483456 | 698.0 | 14.0 | uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef |
| 1 | 2 | Moz | 17/11/2016 | 1 | 2017-04-02T09:48:16.000Z | 2017-04-02T17:26:19.000Z | Manica | Manica | Bandula | God | ... | no | ['cow_cart', 'bicycle', 'radio', 'cow_plough',... | 2 | ['Jan', 'Sept', 'Oct', 'Nov', 'Dec'] | ['na', 'reduce_meals', 'restrict_adults', 'bor... | -19.112477 | 33.483416 | 690.0 | 19.0 | uuid:099de9c9-3e5e-427b-8452-26250e840d6e |
| 2 | 3 | Moz | 17/11/2016 | 3 | 2017-04-02T14:35:26.000Z | 2017-04-02T17:26:53.000Z | Manica | Manica | Bandula | God | ... | no | ['solar_torch'] | 2 | ['Jan', 'Feb', 'Mar', 'Oct', 'Nov', 'Dec'] | ['na', 'restrict_adults', 'lab_ex_food'] | -19.112108 | 33.483450 | 674.0 | 13.0 | uuid:193d7daf-9582-409b-bf09-027dd36f9007 |
| 3 | 4 | Moz | 17/11/2016 | 4 | 2017-04-02T14:55:18.000Z | 2017-04-02T17:27:16.000Z | Manica | Manica | Bandula | God | ... | no | ['bicycle', 'radio', 'cow_plough', 'solar_pane... | 2 | ['Sept', 'Oct', 'Nov', 'Dec'] | ['na', 'reduce_meals', 'restrict_adults', 'lab... | -19.112229 | 33.483424 | 679.0 | 5.0 | uuid:148d1105-778a-4755-aa71-281eadd4a973 |
| 4 | 5 | Moz | 17/11/2016 | 5 | 2017-04-02T15:10:35.000Z | 2017-04-02T17:27:35.000Z | Manica | Manica | Bandula | God | ... | no | ['motorcyle', 'radio', 'cow_plough', 'mobile_p... | 2 | ['Aug', 'Sept', 'Oct', 'Nov'] | ['na', 'go_forest', 'migrate'] | -19.112217 | 33.483425 | 689.0 | 10.0 | uuid:2c867811-9696-4966-9866-f35c3e97d02d |
| 5 | 6 | Moz | 17/11/2016 | 6 | 2017-04-02T15:27:25.000Z | 2017-04-02T17:28:02.000Z | Manica | Manica | Bandula | God | ... | no | None | 2 | ['Aug', 'Sept', 'Oct'] | ['borrow_food', 'lab_ex_food', 'seek_government'] | -19.112196 | 33.483392 | 692.0 | 12.0 | uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70 |
| 6 | 7 | Moz | 17/11/2016 | 7 | 2017-04-02T15:38:01.000Z | 2017-04-02T17:28:19.000Z | Manica | Manica | Bandula | God | ... | no | ['motorcyle', 'cow_plough'] | 3 | ['Nov'] | ['lab_ex_food'] | -19.112219 | 33.483365 | 709.0 | 11.0 | uuid:ae20a58d-56f4-43d7-bafa-e7963d850844 |
| 7 | 8 | Moz | 16/11/2016 | 8 | 2017-04-02T15:59:52.000Z | 2017-04-02T17:28:39.000Z | Manica | Manica | Manica | Chirodzo | ... | no | ['motorcyle', 'bicycle', 'television', 'radio'... | 2 | ['Jan'] | ['rely_less_food', 'limit_variety', 'reduce_me... | -19.112160 | 33.483419 | 700.0 | 9.0 | uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a |
| 8 | 9 | Moz | 16/11/2016 | 9 | 2017-04-02T16:23:36.000Z | 2017-04-02T16:42:08.000Z | Manica | Manica | Bandula | Chirodzo | ... | no | ['television', 'solar_panel', 'solar_torch'] | 3 | ['Jan', 'Dec'] | ['rely_less_food', 'limit_variety', 'limit_por... | -19.112215 | 33.483437 | 701.0 | 11.0 | uuid:846103d2-b1db-4055-b502-9cd510bb7b37 |
| 9 | 10 | Moz | 16/12/2016 | 10 | 2017-04-02T17:03:28.000Z | 2017-04-02T17:25:11.000Z | Manica | Manica | Bandula | Chirodzo | ... | no | ['cow_cart', 'motorcyle', 'bicycle', 'televisi... | 3 | ['Jan', 'Oct', 'Nov', 'Dec'] | ['rely_less_food', 'limit_portion', 'restrict_... | -19.112215 | 33.483394 | 710.0 | 14.0 | uuid:8f4e49bc-da81-4356-ae34-e0d794a23721 |
10 rows × 61 columns
Try it Yourself!
How would you return only the first 5 rows of the columns about food?
We can use a “where” keyword to filter and only choose a subset of the rows conditionally, based on values of the data. For example, we can look only at the farms that have living parents.
parents_df = pd.read_sql_query("SELECT Id, B17_parents_liv FROM Farms WHERE B17_parents_liv = 'yes'",con)
parents_df.head()
| Id | B17_parents_liv | |
|---|---|---|
| 0 | 2 | yes |
| 1 | 5 | yes |
| 2 | 7 | yes |
| 3 | 8 | yes |
| 4 | 9 | yes |
Try it yourself¶
Write a queries to read from the database, the Id column and two subsets of columns to 2 separate dataframes. Then use pandas to merge the dataframes into one.