- Jackson Park
- Make sure to see the japanese garden, and the columbia statue
- Also the MSI
- Smart is also good
- go have breakfast at Daley's 2nd oldest resturant in the city
- Calumet and the east side is cool - check out the state line power plant and have some Calumet Fisheries
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# coding: utf-8 | |
import pandas as pd | |
# Get the table by id, pick first item on the returned list | |
df = pd.read_html('http://primis.phmsa.dot.gov/comm/reports/operator/OperatorIM_opid_300.html?nocache=6511#_Incidents_tab_3', attrs={'id': '_4E112845Q'})[0] | |
# only in CA | |
df[df['State'] == 'CA'].keys() | |
# Sort by property damage |
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import pymssql | |
conn = pymssql.connect(server="", user="",password="", port=) | |
stmt = "SELECT * FROM AlumniMirror..vwApp_Detail WHERE applied_dummy = 1;" | |
df = pd.read_sql(stmt,conn) |
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import pandas as pd | |
import datetime | |
df = pd.read_csv('./path_to_file.csv') | |
df['date'] = df['datetime'].apply(datetime.datetime.fromtimestamp) | |
df.to_csv('new_file') |
Routes are in rough order of speed.
- Chicago - Quad Cities - Des Moines - Omaha - Denver - Grand Junction - Vegas - LA
- Pretty much just the fastest way
- Go to the art museum in the Quad Cities and John Deree Museum
- Lotus of Siam in Vegas
- not close enough to too many national parks, but you could maybe hit Zion.
- Chicago - Springfield - Kansas City - OKC - ABQ - Flagstaff - Joshua Tree - LA
- This is how I moved back to LA, and the only drive I made solo. Lots of great food.
- ABQ is amazing, spent a day there.
Routes are in rough order of speed.
- Chicago - Quad Cities - Des Moines - Omaha - Denver - Grand Junction - Vegas - LA
- Pretty much just the fastest way
- Go to the art museum in the Quad Cities and John Deree Museum
- Lotus of Siam in Vegas
- not close enough to too many national parks, but you could maybe hit Zion.
- Chicago - Springfield - Kansas City - OKC - ABQ - Flagstaff - Joshua Tree - LA
- This is how I moved back to LA, and the only drive I made solo. Lots of great food.
- ABQ is amazing, spent a day there.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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import pandas as pd | |
df = pd.read_csv('path/to/csv') | |
df.groupby(['CD','RequestType']).count().to_csv('2016_311_counts.csv') |