Python for data science: Getting started

cand_nm
— name of the candidate receiving the donation. 
contbr_nm
— name of the contributor. 
contbr_state
— state where the contributor lives. 
contbr_employer
— where the contributor works. 
contbr_occupation
— the occupation of the contributor. 
contb_receipt_amount
— the size of the contribution, in US dollars. 
contb_receipt_dt
— the date the contribution was received.
Installing Python
The first step we’ll need to take to analyze this data is to install
Python. Installing Python is an easy process with Anaconda, a tool that installs Python along with several popular data analysis libraries. You can download Anaconda here. It’s recommended to install Python 3.5, which is the newest version of Python. You can read more about Python 2 vs Python 3 here. Anaconda automatically installs several libraries that we’ll be using in this post, including Jupyter, Pandas, scikitlearn, and matplotlib.
Getting started with Jupyter
Now that we have everything installed, we can launch Jupyter notebook (formerly known as IPython notebook). Jupyter notebook is a powerful data analysis tool that enables you to quickly explore data, visualize your findings, and share your results. It’s used by data scientists at organizations like Google, IBM, and Microsoft to analyze data and collaborate. Start Jupyter by running
ipython notebook
in the terminal. If you have trouble, check here. You should see a file browser interface that allows you to create new notebooks. Create a Python 3 notebook, which we’ll be using in our analysis. If you need more help with installation, check out our guide here.
Notebook cells
Each Jupyter notebook is composed of multiple
cells in which you can run code or write explanations. Your notebook will only have one cell initially, but you can add more:
# This is a code cell.
Any output we generate here will show up below.
print(10)
b = 10
# You can have multiple cells, and rerun each cell as many times as you want to refine your analysis.
# The power of Jupyter notebook is that the results of each cell you run are cached.
# So you can run code in cells that depends on other cells.
print(b * 10)
If you want to learn more about Jupyter, check our our indepth tutorial
here.
Getting started with Pandas
Pandas is a data analysis library for Python. It enables us to read in data from a variety of formats, including csv, and then analyze that data efficiently. We can read in the data using this code:
import pandas as pd
donations = pd.read_csv("political_donations.csv")
donations.shape
(384885, 18)
donations.head(2)
cmte_id  cand_id  cand_nm  contbr_nm  contbr_city  contbr_st  contbr_zip  contbr_employer  contbr_occupation  contb_receipt_amt  contb_receipt_dt  receipt_desc  memo_cd  memo_text  form_tp  file_num  tran_id  election_tp  

C00458844  P60006723  Rubio, Marco  KIBBLE, KUMAR  DPO  AE  092131903  U.S. DEPARTMENT OF HOMELAND SECURITY  LAW ENFORCEMENT  500  27AUG15  NaN  NaN  NaN  SA17A  1029457  SA17.813360  P2016  NaN 
C00458844  P60006723  Rubio, Marco  HEFFERNAN, MICHAEL  APO  AE  090960009  INFORMATION REQUESTED PER BEST EFFORTS  INFORMATION REQUESTED PER BEST EFFORTS  210  27JUN15  NaN  NaN  NaN  SA17A  1029436  SA17.796904  P2016  NaN 
In the cells above, we import the Pandas library using
import pandas as pd
, then use the read_csv() method to read political_donations.csv
into the donations
variable. The donations
variable is a Pandas DataFrame, which is an enhanced version of a matrix that has data analysis methods built in and allows different datatypes in each column. We access the shape
property of donations
variable to print out how many rows and columns it has. When a statement or variable is placed on the last line of a notebook cell, its value or output is automatically rendered! We then use the head() method on DataFrames to print out the first two rows of donations
so we can inspect them. If you want to dive into more depth with Pandas, see our course here.
Total donations by candidate
We can compute percandidate summary statistics using the Pandas
groupby() method. We can first use the groupby method to split donations
into subsets based on cand_nm
. Then, we can compute statistics separately for each candidate. The first summary statistic we compute will be total donations. To get this, we just take the sum of the contb_receipt_amount
column for each candidate.
donations.groupby("cand_nm").sum().sort("contb_receipt_amt")
In the above code, we first split
donations
into groups based on cand_nm
using the code donations.groupby("cand_nm")
. This returns a GroupBy object that has some special methods for aggregating data. One of these methods is sum()
, which we use to compute the sum of each column in each group. Pandas automatically recognizes the data type of columns when data is read in, and only performs the sum operation on the numeric columns. We end up with a DataFrame showing the sum of the contb_receipt_amt
and file_num
columns for each candidate. In a final step, we use the sort() method on DataFrames to sort in ascending order to contb_receipt_amt
. This shows us how much each candidate has collected in donations.
Visualizing total donations
We can use
matplotlib, the main Python data visualization library, to make plots. Jupyter notebook even supports rendering matplotlib plots inline. We’ll need to activate the inline mode of matplotlib to do this. We can use Jupyter magics to make matplotlib figures show up in the notebook. Magics are commands that start with a %
or %%
, and affect the behavior of Jupyter notebook. They are meant to be used as a way to change Jupyter configuration without the commands getting mixed up with Python code. To enable matplotlib figures to show up inline, we need to run %matplotlib inline
in a cell. Read more about plotting with Jupyter here. Here we import the matplotlib
library and activate inline mode:
import matplotlib.pyplot as plt
Pandas DataFrames have builtin visualization support, and you can call the
plot() method to generate a matplotlib
plot from a DataFrame. This is often much quicker than using matplotlib
directly. First, we assign our DataFrame from earlier to a variable, total_donations
. Then, we use indexing to select a single column of the DataFrame, contb_receipt_amt
. This generates a Pandas Series. Pandas Series have most of the same methods as DataFrames, but they store 1dimensional data, like a single row or a single column. We can then call the plot() method on the Series to generate a bar chart of each candidate’s total donations.
total_donations = donations.groupby("cand_nm").sum().sort("contb_receipt_amt")
total_donations["contb_receipt_amt"].plot(kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x108892208>
If you want to dive more into matplotlib, check out our course
here.
Finding the mean donation size
It’s dead simple to find the mean donation size instead of the total donations. We just swap the
sum()
method for the mean() method.
avg_donations = donations.groupby("cand_nm").mean().sort("contb_receipt_amt")
avg_donations["contb_receipt_amt"].plot(kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x108d82c50>
Predicting donation size
Let’s make a simple algorithm that can figure out how much someone will donate based on their state (
contbr_st
), occupation (contbr_occupation
), and preferred candidate (cand_nm
). The first step is to make a separate Dataframe with just these columns and the contb_receipt_amt
column, which we want to predict.
pdonations = donations[["contbr_st", "contbr_occupation", "cand_nm", "contb_receipt_amt"]]
Now we’ll check the data types of each column in
pdonations
. When Pandas reads in a csv file, it automatically assigns a data type to each column. We can only use columns to make predictions if they are a numeric data type.
pdonations.dtypes
contbr_st object
contbr_occupation object
cand_nm object
contb_receipt_amt float64
dtype: object
Unfortunately, all of the columns we want to use to predict are
object datatypes (strings). This is because they are categorical data. Each column has several options, but they are shown as text instead of using numeric codes. We can convert each column into numeric data by converting to the categorical datatype, then to numeric. Here’s more on the categorical data type. Essentially, the categorical data type assigns a numeric code behind the scenes to each unique value in a column. We can replace the column with these codes to convert to numeric entirely.
pdonations["contbr_st"] = pdonations["contbr_st"].astype('category')
pdonations["contbr_st"] = pdonations["contbr_st"].cat.codes
pdonations["contbr_st"]
0 1
1 1
2 1
3 2
4 2
5 2
6 2
7 2
8 2
9 2
10 2
11 2
12 2
13 2
14 2
...
384870 75
384871 75
384872 75
384873 75
384874 75
384875 75
384876 75
384877 75
384878 75
384879 75
384880 75
384881 75
384882 75
384883 77
384884 77
Name: contbr_st, Length: 384885, dtype: int8
As you can see, we’ve converted the
contbr_st
column to numeric values. We’ll need to repeat the same process with the contbr_occupation
and cand_nm
columns.
for column in ["contbr_st", "contbr_occupation", "cand_nm"]:
pdonations[column] = pdonations[column].astype('category')
pdonations[column] = pdonations[column].cat.codes
Splitting into training and testing sets
We’ll now be able to start leveraging
scikitlearn, the primary Python machine learning library, to help us with the rest of the prediction workflow. First, we’ll split the data into two sets — one which we train our algorithm on called the training set, and one which we use to evaluate the performance of the model called the test set. We do this to avoid overfitting, and getting a misleading error value. We can use the train_test_split() function to split pdonations
into a train and a test set.
from sklearn.cross_validation import train_test_split
train, test, y_train, y_test = train_test_split(pdonations[["contbr_st", "contbr_occupation", "cand_nm"]], pdonations["contb_receipt_amt"], test_size=0.33, random_state=1)
The above code splits the columns we want to use to train the algorithm, and the column we want to make predictions on (
contb_receipt_amt
) each both train and test sets. We take 33%
of the data for the test set. The rows are randomly assigned to the sets.
Fitting a model
We’ll use the
random forest algorithm to make our predictions. It’s an accurate and versatile algorithm, and it’s implemented by scikitlearn via the RandomForestRegressor class. This class makes it simple to train the model, then make predictions with it. First, we’ll train the model using train
and y_train
:
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10)
model.fit(train, y_train)
RandomForestRegressor(bootstrap=True, compute_importances=None, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, min_density=None, min_samples_leaf=10, min_samples_split=2, n_estimators=100, n_jobs=1, oob_score=False, random_state=None, verbose=0)
One of the great things about scikitlearn is that it has a consistent API across all the algorithms it implements. You can train a linear regression the exact same way you train a random forest. We now have a fit model, so we can make predictions with it.
Making predictions and finding error
It’s very easy to make predictions with scikitlearn. We just pass in our test data to the fitted model.
predictions = model.predict(test)
Now that we have predictions, we can calculate error. Our error will let us know how well our model is performing, and give us a way to evaluate it as we make tweaks. We’ll use
mean squared error, a common error metric.
from sklearn.metrics import mean_squared_error
import math
mean_squared_error(predictions, y_test)
756188.21680533944
If you want to learn more about scikitlearn, check out our tutorials
here.
Next steps
Taking the square root of the error you got will give you an error value that’s easier to think about in terms of donation size. If you don’t take the square root, you’ll have the average squared error, which doesn’t directly mean anything in the context of our data. Either way, the error is large, and there are many things you can do to lower it.
 Add in data from more columns.
 See if percandidate models are more accurate.
 Try other algorithms.
Here are some other interesting data explorations you could do:
 Map out which candidates get the most donations from each state.
 Plot which occupations give the most to each candidate.
 Divide candidates by Republican/Democrat, and see if any interesting patterns emerge.
 Assign genders based on name, and see if splitting the data by gender reveals any interesting patterns.
 Make a heatmap of total donations by area of the US.
If you want to dive more deeply into the concepts mentioned here, check out our interactive lessons on
Vik is the CEO and Founder of Dataquest.