import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web
import datetime
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def gather_data(data_codes, start, end = datetime.datetime.today(), freq = "A"):
i = 0
# dct.items() calls key and value that key points to
for key, val in data_codes.items():
if i == 0:
# Create dataframe for first variable, then rename column
df = web.DataReader(val, "fred", start, end).resample(freq).first()
df.rename(columns = {val:key}, inplace = True)
i = None
else:
# If dataframe already exists, add new column
df[key] = web.DataReader(val, "fred", start, end).resample(freq).first()
return df
def plot_lines(df, plot_vars, linewidth = 1, logy = False, figsize = (40,20),
secondary_y = None, pp = None):
fig, ax = plt.subplots(figsize = figsize)
legend_scale = 20 / figsize[1]
# If no secondary_y (axis), plot all variables at once
if secondary_y == None:
df[plot_vars].plot.line(linewidth = linewidth, logy = logy, ax = ax)
ax.legend(bbox_to_anchor=(0, 1.035 + .045 * len(plot_vars) * legend_scale),
loc=2)
# Otherwise, create a new axis and plot each variable individually
else:
ax2 = ax.twinx()
for var in plot_vars:
if var == secondary_y:
df[var].plot.line(linewidth = linewidth, logy = logy, ax = ax2, c = "C9",
label = var + " (right)")
else:
df[var].plot.line(linewidth = linewidth, logy = logy, ax = ax)
# If there are two axes, then gather lines from each axis
lines = ax.get_lines() + ax2.get_lines()
# then gather the label from each line
labels = [l.get_label() for l in lines]
# and use the lines and labels to create the legend
ax.legend(lines, labels, bbox_to_anchor=(0,
1.04 + .045 * len(plot_vars) * legend_scale), loc=2)
# Turn the text on the x-axis so that it reads vertically
ax.tick_params(axis='x', rotation=90)
# Get rid of tick lines perpendicular to the axis for aesthetic
ax.tick_params('both', length=0, which='both')
# save image if PdfPages object was passed
if pp != None: pp.savefig(fig, bbox_inches = "tight")
def plot_scatter(df, plot_vars, s = 75, figsize = (40, 20), pp = None):
# Create plot for every unique pair of variables
for var1 in plot_vars:
for var2 in plot_vars:
if var1 != var2:
fig, ax = plt.subplots(figsize = figsize)
# Create list of years from index
# Year will be represented by color
df["Year"] = [int(str(ind)[:4]) for ind in df.index]
df.plot.scatter(x = var1, y = var2, s = s, ax = ax, c = "Year",
cmap = "viridis")
# Turn the text on the x-axis so that it reads vertically
ax.tick_params(axis='x', rotation=90)
# Get rid of tick lines perpendicular to the axis for aesthetic
ax.tick_params('both', length=0, which='both')
# save image if PdfPages object was passed
if pp != None: pp.savefig(fig, bbox_inches = "tight")
# Create PDF that will hold visualizations
pp = PdfPages("Capital Measures " + str(datetime.datetime.today())[:10] + ".pdf")
# set default fontsize for text in plot
plt.rcParams.update({'font.size': 32})
# Choose data from FRED
# Keys will be used to name variable. Key points to FRED code
data_codes = {"Capital Stock":"RKNANPUSA666NRUG",
"Nominal GDP":"GDP",
"Price Level":"GDPDEF",
"Population":"B230RC0A052NBEA"}
# Select start and end dates
start = datetime.datetime(1950, 1, 1)
end = datetime.datetime(2019, 1, 31)
# Check if data has been gathered.
# If data needs to be gathered again, clear variables or restart kernel
if "data_gathered" not in locals():
df = gather_data(data_codes, start = start,
end = end, freq = "A")
# After data is downloaded create new data and transform data
# set population to millions
df["Population"] = df["Population"] / 10 ** 3
# set NGDP to millions
df["Nominal GDP"] = df["Nominal GDP"] * 10 ** 3
# Make 2011 base year for price level to match capital stock values
df["Price Level"] = df["Price Level"].div(df["Price Level"].loc["2011"].values[0])
# Create RGDP using 2011 prices
df["Real GDP"] = df["Nominal GDP"].div(df["Price Level"])
# Use population as proxy for L
# y = Y / L
df["Real GDP Per Capita"] = df["Real GDP"].div(df["Population"])
# k = K / L
df["Effective Capital Stock"] = df["Capital Stock"].div(df["Population"])
# Estimate Solow's A
# y = Ak**.5
# A = y / k ** .5
df["Efficiency of Capital"] = df["Real GDP Per Capita"].div(df["Effective Capital Stock"] ** (1/2))
data_gathered = True
####### Plot Variables #######
plot_vars = ["Effective Capital Stock"]
plot_lines(df, plot_vars, linewidth = 3, logy = True, pp = pp)
plot_vars = ["Efficiency of Capital", "Real GDP Per Capita"]
plot_lines(df, plot_vars, linewidth = 3, logy = True,
secondary_y = "Efficiency of Capital", pp = pp)
plot_scatter(df, plot_vars, pp = pp)
pp.close()
Some figures generated:
