def quantile_pct_chg_plot(v1, v2, w1=None, w2=None, q=np.arange(0.1, 1, 0.1)):
""" Create stem plot with percent change in decile boundaries.
Args:
v1: First set of values.
v2: Second set of values.
w1: First set of weights. Defaults to equal weight.
w2: Second set of weights. Defaults to equal weight.
q: Quantiles. Defaults to decile boundaries.
Returns:
Axis.
"""
# Calculate weighted quantiles.
df = mdf.quantile_chg(v1, v2, w1, w2, q).transpose()
# Prepare dataset for plotting.
df.columns = ['base', 'reform']
df['pct_chg'] = df.reform / df.base - 1
df['index_newline'] = np.where(df.index == '50th (median)',
'50th\n(median)', df.index)
# Plot.
fig, ax = plt.subplots()
markerline, stemlines, baseline = ax.stem(df.index_newline, df.pct_chg)
plt.setp(baseline, color='gray', linewidth=0)
ax.yaxis.set_major_formatter(
mpl.ticker.FuncFormatter(lambda x, _: '{:.0%}'.format(x)))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))
plt.title('Change to disposable income percentiles', loc='left')
plt.ylabel('Change in disposable income at the percentile boundary')
plt.xlabel('Disposable income percentile')
sns.despine(left=True, bottom=True)
ax.grid(color=mdf.GRID_COLOR, axis='y')
plt.xticks(rotation=0)
return ax
def quantile_chg_plot(df1,
df2,
col1,
col2,
w1=None,
w2=None,
q=None,
label1="Base",
label2="Reform",
title="Change in disposable income percentiles",
currency="USD"):
"""Create plot with one line per quantile boundary between base and
reform.
:param df1: DataFrame with first set of values.
:param df2: DataFrame with second set of values.
:param col1: Name of columns with values in df1.
:param col2: Name of columns with values in df2.
:param w1: Name of weight column in df1.
:param w2: Name of weight column in df2.
:param q: Quantiles. Defaults to decile boundaries.
:param label1: Label for left side of x-axis. Defaults to 'Base'.
:param label2: Label for right side of x-axis. Defaults to 'Reform'.
:returns: Axis.
"""
# Calculate the q default because it's later used for defining a color
# palette.
if q is None:
q = np.arange(0.1, 1, 0.1)
# Calculate weighted quantiles.
df = mdf.quantile_chg(df1, df2, col1, col2, w1, w2, q)
# Make shades of green, removing the lightest 10 shades.
with sns.color_palette(sns.color_palette("Greens", q.size + 11)[11:]):
ax = df.plot()
# Label the start and end points.
plt.xticks([0, 1], [label1, label2])
# Label the lines instead of using a legend.
ax.get_legend().remove()
# Move line labels closer to the center.
ll.labelLines(plt.gca().get_lines(), xvals=(0.1, 0.9))
# Formatting.
plt.title(title)
plt.ylim(0, None)
ax.grid(color=mdf.GRID_COLOR, axis="y")
formatter = mdf.currency_format(currency)
ax.yaxis.set_major_formatter(formatter)
ax.yaxis.set_minor_formatter(formatter)
sns.despine(left=True, bottom=True)
ax.axhline(0, color="lightgray", zorder=-1)
# Looks better narrower.
plt.gcf().set_size_inches(4, 6)
return ax
def quantile_chg_plot(v1,
v2,
w1=None,
w2=None,
q=np.arange(0.1, 1, 0.1),
label1='Base',
label2='Reform'):
""" Create plot with one line per quantile boundary between base and
reform.
Args:
v1: First set of values.
v2: Second set of values.
w1: First set of weights. Defaults to equal weight.
w2: Second set of weights. Defaults to equal weight.
q: Quantiles. Defaults to decile boundaries.
label1: Label for left side of x-axis. Defaults to 'Base'.
label2: Label for right side of x-axis. Defaults to 'Reform'.
Returns:
Axis.
"""
# Calculate weighted quantiles.
df = mdf.quantile_chg(v1, v2, w1, w2, q)
# Make shades of green, removing the lightest 10 shades.
with sns.color_palette(sns.color_palette('Greens', q.size + 11)[11:]):
ax = df.plot()
# Label the start and end points.
plt.xticks([0, 1], [label1, label2])
# Label the lines instead of using a legend.
ax.get_legend().remove()
# Move line labels closer to the center.
ll.labelLines(plt.gca().get_lines(), xvals=(0.1, 0.9))
# Formatting.
plt.title('Change in disposable income percentiles')
plt.ylim(0, None)
ax.grid(color=mdf.GRID_COLOR, axis='y')
ax.yaxis.set_major_formatter(mdf.dollar_format())
ax.yaxis.set_minor_formatter(mdf.dollar_format())
sns.despine(left=True, bottom=True)
ax.axhline(0, color='lightgray', zorder=-1)
# Looks better narrower.
plt.gcf().set_size_inches(4, 6)
return ax
def quantile_pct_chg_plot(df1, df2, col1, col2, w1=None, w2=None, q=None):
"""Create stem plot with percent change in decile boundaries.
:param df1: DataFrame with first set of values.
:param df2: DataFrame with second set of values.
:param col1: Name of columns with values in df1.
:param col2: Name of columns with values in df2.
:param w1: Name of weight column in df1.
:param w2: Name of weight column in df2.
:param q: Quantiles. Defaults to decile boundaries.
:returns: Axis.
"""
if q is None:
q = np.arange(0.1, 1, 0.1)
# Calculate weighted quantiles.
df = mdf.quantile_chg(df1, df2, col1, col2, w1, w2, q).transpose()
# Prepare dataset for plotting.
df.columns = ["base", "reform"]
df["pct_chg"] = df.reform / df.base - 1
# Multiply by 100 pending github.com/matplotlib/matplotlib/issues/17113
df.pct_chg *= 100
df["index_newline"] = np.where(
df.index == "50th (median)", "50th\n(median)", df.index
)
# Plot.
fig, ax = plt.subplots()
markerline, stemlines, baseline = ax.stem(
df.index_newline, df.pct_chg, use_line_collection=True
)
plt.setp(baseline, color="gray", linewidth=0)
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))
ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=100))
plt.title("Change to percentiles", loc="left")
plt.ylabel("Change at the percentile boundary")
plt.xlabel("Percentile")
sns.despine(left=True, bottom=True)
ax.grid(color=mdf.GRID_COLOR, axis="y")
plt.xticks(rotation=0)
return ax
def test_quantile_chg():
mdf.quantile_chg(DF1, DF2, 'v', 'w', 'v', 'w')
def test_quantile_chg():
mdf.quantile_chg(DF1, DF2, "v", "w", "v", "w")
def test_quantile_chg():
mdf.quantile_chg(v1=V1, v2=V2, w1=W1, w2=W2)