Hunter Owens

Python for Data Analysis - Workshop

31 Oct 2013

Below are the notes/key points from the Workshop I gave to Hack@UChicago on using Python for Data Analysis. Many thanks to all those who helped me put it togehter. The source is avaliable on Github.

Python For Data Analysis

Data Structures

There are two important data structures in Pandas.

The Series

The series is a 'one dimesional array like object containing an array of data (of any Numpy data type) and an associated array of data labels, called an index.

``` obj = Series([4,5,6,'a string'])

``` This example is only an array of data.

``` obj2 = Series([4,5,6,'a string'],index=['a','b','c','d'])

``` This example has a labeled index.

A series can be though of as a fixed length order dict. Frequently, you will want to take some dictionary an put it into a series. You can also use a series in many functions that take dicts.

food_trucks = {'La Adelita': 'Mexican', 'King Schnizel':'German','Bridgeport Pasty':'German'} food_truck_series = Series(food_trucks)

The Dataframe

The dataframe is a 2-dimesional array- think of it as a spreadsheet. You can use hierachical ordering to achieve multi-dimesional arrays. (We won't be covering that, though)

data = {'building':['Ryerson','Ryerson','Pick', 'Harper'], 'room':[251,276,532,140], 'capacity':[153,54,15,96]} df = DataFrame(data)

There are many different indexs you can set on data frames.

Loading Data

Pandas makes it super easy to load data. There are build in methods such as read_csv & load (for pickle files). You can also easily load at dict into a data frame, providing an easy way to interact with JSON REST API's on the Web. How easy? This easy

``` url = '' resp = requests.get(url)

data = json.loads(resp.text) stationsmetadata = data['stationBeanList'] stationsdataFrame = DataFrame(stations_metadata) Or, in one line r = DataFrame(json.loads((requests.get('')).text)['stationBeanList']) ```

Not Covered in the This Workshop: Loading Data from Databases. However, there are several built in methods for creating dataframes from SQL and NoSQL Databases.

Cleaning Data

  1. Get Dataset
  2. Clean Dataset
  3. Repeat.

Anyways, one of the first things you need to do when you recieve a Dataset is clean it/validate it.

Pandas provides a set of built in methods for handling missing and erroronous data. Please consult either the documentation for Python for Data Analysis by Wes Mckinney.

Sorting Data

One of the first thinks that you might do is sort the data into several different DataFrames. With the Divvy Dataset, we have station id and records at every minute.

Merging Data

Database styles joins are possible with Pandas. You can Join 2 Dataframes, and or a DataFrame+Sequences. There are left, right, and inner joins. Here is a key join of two dataframes.

mergeset = pd.merge(september_dataframe,ldata,left_on='station_id',right_on='id')

However, you may also want to concatenate data. For those of you familar with Numpy arrays, Pandas includes the .concat method to allow concatination in the same way.

Graphing Data

Pandas integrates well with the standard MatPlotLib graphing libary. Let's say we want to get the average number of bikes in all the stations for each minute.

First, you need to convert the timestamps.2. september_dataframe['timestamp'] = september_dataframe['timestamp'].apply(lambda t: pd.to_datetime())

Then, Group by minute value (i.e. how many minutes have occured since midnight)

station_monthly_groups = september_dataframe.groupby(september_dataframe['timestamp'].map(lambda t: 60*t.hour + t.minute)

Take the average of the group

station_monthly_averages = station_monthly_groups.mean() station_monthly_std = station_monthly_groups["bikes"]std(

Time readability conversions.

times = times_std =

add to dataframe.

station_monthly_averages["timestamp"] = times station_monthly_averages["bikes_available_std"] = station_monthly_td

Now, plot

grid_size = (1,1) count = 1 nb_plots_per_page = 1 ax = plt.subplot2grid(grid_size, (count % nb_plots_per_page,0)) t = pd.to_datetime(station_monthly_averages['timestamp']) mu1 = station_montly_averages['bikes'] sigma1 = station_monthly_averages['bikes_available_std'] ax.plot(t, mu1)

Mapping Data

Pandas/Matplotlib/Basemap can also map data based on a shapefile. Basemap is an extention for matplotlib that utilizes the geos library to map cordinates, lines, etc.

Mac Users can install Geos/Basemap by running brew install geos and then python build and then python install in the directory in which they unzipped Basemap.

To start with map data, you're going to want to have a basic map obj. This is a basic stere projection of Chicago.

def basic_map(ax=None,lllat=41.75,urlat=42, lllon=-88,urlon=-87.5): ##create polor sterographic Basemap instance m = Basemap(ax=ax, projection='stere', lon_0=(urlon + lllon) / 2, lat_0=(urlat + lllat) / 2, llcrnrlat = lllat, urcrnrlat=urlat, llcrnrlon = lllon, urcrnrlon = urlon, resolution = 'f') m.drawcoastlines() m.drawstates() m.drawcountries() return m m = basic_map() Now, we want to plot each divvy station point on the map. To do this, we iterate over the dataframe and then plot the lat/long.

for index, row in stations_dataframe.iterrows(): x,y = m(row['longitude'], row['latitude']) m.plot(x,y,'k.',alpha=1)

That's it. (except, we may want to add, say, city of chicago streets.) To do that, we need to load a shape. Open Street Map provides shapes for the state of illinois. Download those and unzip them, and add

shapefile = ('./data/illinois-latest/illinois-latest') m.readshapefile(shapefile, 'roads')

which will render the streets.