Geocoding And Reverse Geocoding Geocoding (converting a physical address or location into latitude/longitude) and reverse geocoding (converting a lat/long to a physical address or location) are common tasks when working with geo-data. Python offers a number of packages to make the task incredibly easy. In the tutorial below, I use pygeocoder, a wrapper for Google’s …
Find Unique Values In Pandas Dataframes import pandas as pd import numpy as np raw_data = {‘regiment’: [’51st’, ’29th’, ‘2nd’, ’19th’, ’12th’, ‘101st’, ’90th’, ’30th’, ‘193th’, ‘1st’, ’94th’, ’91th’], ‘trucks’: [‘MAZ-7310’, np.nan, ‘MAZ-7310’, ‘MAZ-7310’, ‘Tatra 810’, ‘Tatra 810’, ‘Tatra 810’, ‘Tatra 810’, ‘ZIS-150’, ‘Tatra 810’, ‘ZIS-150’, ‘ZIS-150’], ‘tanks’: [‘Merkava Mark 4’, ‘Merkava Mark 4’, ‘Merkava …
Find Largest Value In A Dataframe Column /* import modules */ %matplotlib inline import pandas as pd import matplotlib.pyplot as plt import numpy as np /* Create dataframe */ raw_data = {‘first_name’: [‘Jason’, ‘Molly’, ‘Tina’, ‘Jake’, ‘Amy’], ‘last_name’: [‘Miller’, ‘Jacobson’, ‘Ali’, ‘Milner’, ‘Cooze’], ‘age’: [42, 52, 36, 24, 73], ‘preTestScore’: [4, 24, 31, 2, 3], …
Filter pandas Dataframes Import modules import pandas as pd Create Dataframe data = {‘name’: [‘Jason’, ‘Molly’, ‘Tina’, ‘Jake’, ‘Amy’], ‘year’: [2012, 2012, 2013, 2014, 2014], ‘reports’: [4, 24, 31, 2, 3], ‘coverage’: [25, 94, 57, 62, 70]} df = pd.DataFrame(data, index = [‘Cochice’, ‘Pima’, ‘Santa Cruz’, ‘Maricopa’, ‘Yuma’]) df coverage name reports year Cochice 25 …
Dropping Rows And Columns In pandas Dataframe Import modules import pandas as pd Create a dataframe data = {‘name’: [‘Jason’, ‘Molly’, ‘Tina’, ‘Jake’, ‘Amy’], ‘year’: [2012, 2012, 2013, 2014, 2014], ‘reports’: [4, 24, 31, 2, 3]} df = pd.DataFrame(data, index = [‘Cochice’, ‘Pima’, ‘Santa Cruz’, ‘Maricopa’, ‘Yuma’]) df name reports year Cochice Jason 4 2012 …
Crosstabs In pandas Import pandas import pandas as pd raw_data = {‘regiment’: [‘Nighthawks’, ‘Nighthawks’, ‘Nighthawks’, ‘Nighthawks’, ‘Dragoons’, ‘Dragoons’, ‘Dragoons’, ‘Dragoons’, ‘Scouts’, ‘Scouts’, ‘Scouts’, ‘Scouts’], ‘company’: [‘infantry’, ‘infantry’, ‘cavalry’, ‘cavalry’, ‘infantry’, ‘infantry’, ‘cavalry’, ‘cavalry’,’infantry’, ‘infantry’, ‘cavalry’, ‘cavalry’], ‘experience’: [‘veteran’, ‘rookie’, ‘veteran’, ‘rookie’, ‘veteran’, ‘rookie’, ‘veteran’, ‘rookie’,’veteran’, ‘rookie’, ‘veteran’, ‘rookie’], ‘name’: [‘Miller’, ‘Jacobson’, ‘Ali’, ‘Milner’, ‘Cooze’, ‘Jacon’, …
Creating Lists From Dictionary Keys And Values Create a dictionary dict = {‘county’: [‘Cochice’, ‘Pima’, ‘Santa Cruz’, ‘Maricopa’, ‘Yuma’], ‘year’: [2012, 2012, 2013, 2014, 2014], ‘fireReports’: [4, 24, 31, 2, 3]} Create a list from the dictionary keys /* Create a list of keys */ list(dict.keys()) [‘fireReports’, ‘year’, ‘county’] Create a list from the dictionary …
Create a Column Based on a Conditional in pandas Preliminaries /* Import required modules */ import pandas as pd import numpy as np Make a dataframe data = {‘name’: [‘Jason’, ‘Molly’, ‘Tina’, ‘Jake’, ‘Amy’], ‘age’: [42, 52, 36, 24, 73], ‘preTestScore’: [4, 24, 31, 2, 3], ‘postTestScore’: [25, 94, 57, 62, 70]} df = pd.DataFrame(data, …
Create Counts Of Items Preliminaries from collections import Counter Create A Counter /* Create a counter of the fruits eaten today */ fruit_eaten = Counter([‘Apple’, ‘Apple’, ‘Apple’, ‘Banana’, ‘Pear’, ‘Pineapple’]) /* View counter */ fruit_eaten Counter({‘Apple’: 3, ‘Banana’: 1, ‘Pear’: 1, ‘Pineapple’: 1}) Update The Count For An Element /* Update the count for ‘Pineapple’ …
Create A pandas Column With A For Loop Preliminaries import pandas as pd import numpy as np Create an example dataframe raw_data = {‘student_name’: [‘Miller’, ‘Jacobson’, ‘Ali’, ‘Milner’, ‘Cooze’, ‘Jacon’, ‘Ryaner’, ‘Sone’, ‘Sloan’, ‘Piger’, ‘Riani’, ‘Ali’], ‘test_score’: [76, 88, 84, 67, 53, 96, 64, 91, 77, 73, 52, np.NaN]} df = pd.DataFrame(raw_data, columns = [‘student_name’, …