def grade_pie(trials_df,
ax=None,
figsize=None,
colors=PIE_COLORS,
text_size=16):
tight = False
if ax is None:
if figsize is None:
figsize = (5, 5)
pyplot.figure(figsize=figsize)
ax = pyplot.axes()
tight = True
perfect = sum(trials_df["grade_value"] == 10)
correct = sum(trials_df["grade_value"] >= 7) - perfect
common_error = sum(trials_df["grade_category"].str.startswith("common"))
incorrect = len(trials_df) - perfect - correct - common_error
bins = [perfect, correct, incorrect, common_error]
patches, _, _ = ax.pie(bins,
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
if tight:
ax.figure.tight_layout()
ax.legend(patches, ["Perfect", "Correct", "Incorrect", "Common Error"],
loc="lower left",
ncol=2)
return ax
def grade_pie(trials_df, ax=None, figsize=None, colors=PIE_COLORS, text_size=16):
tight = False
if ax is None:
if figsize is None:
figsize = (5, 5)
pyplot.figure(figsize=figsize)
ax = pyplot.axes()
tight = True
perfect = sum(trials_df["grade_value"] == 10)
correct = sum(trials_df["grade_value"] >= 7) - perfect
common_error = sum(trials_df["grade_category"].str.startswith("common"))
incorrect = len(trials_df) - perfect - correct - common_error
bins = [perfect, correct, incorrect, common_error]
patches, _, _ = ax.pie(bins, autopct="%1.1f%%", shadow=False, colors=colors)
shade_axis(ax, size=text_size)
if tight:
ax.figure.tight_layout()
ax.legend(patches, ["Perfect", "Correct", "Incorrect", "Common Error"], loc="lower left", ncol=2)
return ax
def grade_pie(df, base, versions):
"""Pie chart of grade distribution for all versions of a program"""
perfect = 0
correct = 0
common_error = 0
incorrect = 0
fig = pyplot.figure(figsize=(len(versions) * 5, 5))
for i, v in enumerate(versions):
v_rows = df[(df["base"] == base) & (df["version"] == v)]
ax = fig.add_subplot(1, len(versions), i + 1)
ax.set_title(v)
for _, row in v_rows.iterrows():
grade_category = row["grade_category"]
grade_value = int(row["grade_value"])
if grade_value == 10:
perfect += 1
elif grade_value >= 7:
correct += 1
elif "common" in grade_category:
common_error += 1
else:
incorrect += 1
bins = [perfect, correct, incorrect, common_error]
patches, _, _ = ax.pie(bins, autopct="%1.1f%%", shadow=False, colors=colors)
shade_axis(ax)
# fig.suptitle(base)
pyplot.tight_layout()
pyplot.legend(patches, ["Perfect", "Correct", "Incorrect", "Common Error"], loc="lower left", ncol=2)
pyplot.savefig("plots/grade_pie-{0}.png".format(base))
def grade_pie(df, base, versions):
"""Pie chart of grade distribution for all versions of a program"""
perfect = 0
correct = 0
common_error = 0
incorrect = 0
fig = pyplot.figure(figsize=(len(versions) * 5, 5))
for i, v in enumerate(versions):
v_rows = df[(df["base"] == base) & (df["version"] == v)]
ax = fig.add_subplot(1, len(versions), i + 1)
ax.set_title(v)
for _, row in v_rows.iterrows():
grade_category = row["grade_category"]
grade_value = int(row["grade_value"])
if grade_value == 10:
perfect += 1
elif grade_value >= 7:
correct += 1
elif "common" in grade_category:
common_error += 1
else:
incorrect += 1
bins = [perfect, correct, incorrect, common_error]
patches, _, _ = ax.pie(bins,
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax)
#fig.suptitle(base)
pyplot.tight_layout()
pyplot.legend(patches, ["Perfect", "Correct", "Incorrect", "Common Error"],
loc="lower left",
ncol=2)
pyplot.savefig("plots/grade_pie-{0}.png".format(base))
def demographics(experiments, font_family=["Arial"], colors=PIE_COLORS, text_size=14, title_size=18, figsize=(16, 9)):
fig = pyplot.figure(figsize=figsize)
# Bin and plot ages
ax = pyplot.subplot(2, 3, 1)
ax.set_title("Ages", family=font_family, size=title_size)
ages = experiments["age"]
age_bins = [0, 0, 0, 0, 0]
age_bins[0] = len(ages[ages <= 20])
age_bins[1] = len(ages[(20 < ages) & (ages < 25)])
age_bins[2] = len(ages[(25 <= ages) & (ages <= 30)])
age_bins[3] = len(ages[(30 < ages) & (ages <= 35)])
age_bins[4] = len(ages[35 < ages])
ax.pie(
age_bins,
labels=["$18-20$", "$20-24$", "$25-30$", "$31-35$", "$> 35$"],
autopct="%1.1f%%",
shadow=False,
colors=colors,
)
shade_axis(ax, size=text_size)
# Bin and plot Python experience
ax = pyplot.subplot(2, 3, 2)
ax.set_title("Years of\nPython Experience", family=font_family, size=title_size)
py = experiments["py_years"]
py_bins = [0, 0, 0, 0, 0]
py_bins[0] = len(py[py < 0.5])
py_bins[1] = len(py[(0.5 <= py) & (py <= 1)])
py_bins[2] = len(py[(1 < py) & (py <= 2)])
py_bins[3] = len(py[(2 < py) & (py <= 5)])
py_bins[4] = len(py[5 < py])
ax.pie(
py_bins,
labels=["$< 1/2$", "$1/2-1$", "$1-2$", "$2-5$", "$> 5$"],
autopct="%1.1f%%",
shadow=False,
colors=colors,
)
shade_axis(ax, size=text_size)
# Bin and plot programming experience
ax = pyplot.subplot(2, 3, 3)
ax.set_title("Years of\nProgramming Experience", family=font_family, size=title_size)
prog = experiments["prog_years"]
prog_bins = [0, 0, 0, 0, 0]
prog_bins[0] = len(prog[prog < 2])
prog_bins[1] = len(prog[(2 <= prog) & (prog <= 3)])
prog_bins[2] = len(prog[(3 < prog) & (prog <= 5)])
prog_bins[3] = len(prog[(5 < prog) & (prog <= 10)])
prog_bins[4] = len(prog[10 < prog])
ax.pie(
prog_bins,
labels=["$< 2$", "$2-3$", "$3-5$", "$5-10$", "$> 10$"],
autopct="%1.1f%%",
shadow=False,
colors=colors,
)
shade_axis(ax, size=text_size)
# Bin and plot education
ax = pyplot.subplot(2, 3, 4)
ax.set_title("Highest Degree\nReceived", family=font_family, size=title_size)
degrees = experiments["degree"].value_counts()
ax.pie(
degrees.values, labels=[x.capitalize() for x in degrees.keys()], autopct="%1.1f%%", shadow=False, colors=colors
)
shade_axis(ax, size=text_size)
# Bin and plot gender
ax = pyplot.subplot(2, 3, 5)
ax.set_title("Gender", family=font_family, size=title_size)
genders = experiments["gender"].value_counts()
ax.pie(
genders.values, labels=[x.capitalize() for x in genders.keys()], autopct="%1.1f%%", shadow=False, colors=colors
)
shade_axis(ax, size=text_size)
# Bin and plot CS major
ax = pyplot.subplot(2, 3, 6)
ax.set_title("CS Major", family=font_family, size=title_size)
cs_majors = experiments["cs_major"].value_counts()
ax.pie(
cs_majors.values,
labels=[x.capitalize() for x in cs_majors.keys()],
autopct="%1.1f%%",
shadow=False,
colors=colors,
)
shade_axis(ax, size=text_size)
fig.tight_layout()
fig.subplots_adjust(left=0.05, wspace=0.5)
return fig
def demographics(experiments,
font_family=["Arial"],
colors=PIE_COLORS,
text_size=14,
title_size=18,
figsize=(16, 9)):
fig = pyplot.figure(figsize=figsize)
# Bin and plot ages
ax = pyplot.subplot(2, 3, 1)
ax.set_title("Ages", family=font_family, size=title_size)
ages = experiments["age"]
age_bins = [0, 0, 0, 0, 0]
age_bins[0] = len(ages[ages <= 20])
age_bins[1] = len(ages[(20 < ages) & (ages < 25)])
age_bins[2] = len(ages[(25 <= ages) & (ages <= 30)])
age_bins[3] = len(ages[(30 < ages) & (ages <= 35)])
age_bins[4] = len(ages[35 < ages])
ax.pie(age_bins,
labels=["$18-20$", "$20-24$", "$25-30$", "$31-35$", "$> 35$"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot Python experience
ax = pyplot.subplot(2, 3, 2)
ax.set_title("Years of\nPython Experience",
family=font_family,
size=title_size)
py = experiments["py_years"]
py_bins = [0, 0, 0, 0, 0]
py_bins[0] = len(py[py < .5])
py_bins[1] = len(py[(.5 <= py) & (py <= 1)])
py_bins[2] = len(py[(1 < py) & (py <= 2)])
py_bins[3] = len(py[(2 < py) & (py <= 5)])
py_bins[4] = len(py[5 < py])
ax.pie(py_bins,
labels=["$< 1/2$", "$1/2-1$", "$1-2$", "$2-5$", "$> 5$"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot programming experience
ax = pyplot.subplot(2, 3, 3)
ax.set_title("Years of\nProgramming Experience",
family=font_family,
size=title_size)
prog = experiments["prog_years"]
prog_bins = [0, 0, 0, 0, 0]
prog_bins[0] = len(prog[prog < 2])
prog_bins[1] = len(prog[(2 <= prog) & (prog <= 3)])
prog_bins[2] = len(prog[(3 < prog) & (prog <= 5)])
prog_bins[3] = len(prog[(5 < prog) & (prog <= 10)])
prog_bins[4] = len(prog[10 < prog])
ax.pie(prog_bins,
labels=["$< 2$", "$2-3$", "$3-5$", "$5-10$", "$> 10$"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot education
ax = pyplot.subplot(2, 3, 4)
ax.set_title("Highest Degree\nReceived",
family=font_family,
size=title_size)
degrees = experiments["degree"].value_counts()
ax.pie(degrees.values,
labels=[x.capitalize() for x in degrees.keys()],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot gender
ax = pyplot.subplot(2, 3, 5)
ax.set_title("Gender", family=font_family, size=title_size)
genders = experiments["gender"].value_counts()
ax.pie(genders.values,
labels=[x.capitalize() for x in genders.keys()],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot CS major
ax = pyplot.subplot(2, 3, 6)
ax.set_title("CS Major", family=font_family, size=title_size)
cs_majors = experiments["cs_major"].value_counts()
ax.pie(cs_majors.values,
labels=[x.capitalize() for x in cs_majors.keys()],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
fig.tight_layout()
fig.subplots_adjust(left=0.05, wspace=0.5)
return fig
def demographics(exp_df):
"""Pie chart of participant demographics"""
pyplot.figure(figsize=(12, 12))
# Bin and plot ages
ax = pyplot.subplot(2, 2, 1)
ax.set_title("Ages", family=font_family, size=title_size)
ages = exp_df["age"]
age_bins = [0, 0, 0, 0, 0]
age_bins[0] = len(ages[ages <= 20])
age_bins[1] = len(ages[(20 < ages) & (ages < 25)])
age_bins[2] = len(ages[(25 <= ages) & (ages <= 30)])
age_bins[3] = len(ages[(30 < ages) & (ages <= 35)])
age_bins[4] = len(ages[35 < ages])
ax.pie(
age_bins, labels=["18-20", "20-24", "25-30", "31-35", "> 35"], autopct="%1.1f%%", shadow=False, colors=colors
)
shade_axis(ax, size=text_size)
# Bin and plot Python experience
ax = pyplot.subplot(2, 2, 2)
ax.set_title("Years of\nPython Experience", family=font_family, size=title_size)
py = exp_df["py_years"]
py_bins = [0, 0, 0, 0, 0]
py_bins[0] = len(py[py < 0.5])
py_bins[1] = len(py[(0.5 <= py) & (py <= 1)])
py_bins[2] = len(py[(1 < py) & (py <= 2)])
py_bins[3] = len(py[(2 < py) & (py <= 5)])
py_bins[4] = len(py[5 < py])
ax.pie(py_bins, labels=["< 1/2", "1/2-1", "1-2", "2-5", "> 5"], autopct="%1.1f%%", shadow=False, colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot programming experience
ax = pyplot.subplot(2, 2, 3)
ax.set_title("Years of\nProgramming Experience", family=font_family, size=title_size)
prog = exp_df["prog_years"]
prog_bins = [0, 0, 0, 0, 0]
prog_bins[0] = len(prog[prog < 2])
prog_bins[1] = len(prog[(2 <= prog) & (prog <= 3)])
prog_bins[2] = len(prog[(3 < prog) & (prog <= 5)])
prog_bins[3] = len(prog[(5 < prog) & (prog <= 10)])
prog_bins[4] = len(prog[10 < prog])
ax.pie(prog_bins, labels=["< 2", "2-3", "3-5", "5-10", "> 10"], autopct="%1.1f%%", shadow=False, colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot education
ax = pyplot.subplot(2, 2, 4)
ax.set_title("Highest Degree\nReceived", family=font_family, size=title_size)
degrees = exp_df["degree"].value_counts()
ax.pie(
degrees.values, labels=[x.capitalize() for x in degrees.keys()], autopct="%1.1f%%", shadow=False, colors=colors
)
shade_axis(ax, size=text_size)
pyplot.tight_layout()
pyplot.savefig("plots/demographics.png")
def demographics(exp_df):
"""Pie chart of participant demographics"""
pyplot.figure(figsize=(12, 12))
# Bin and plot ages
ax = pyplot.subplot(2, 2, 1)
ax.set_title("Ages", family=font_family, size=title_size)
ages = exp_df["age"]
age_bins = [0, 0, 0, 0, 0]
age_bins[0] = len(ages[ages <= 20])
age_bins[1] = len(ages[(20 < ages) & (ages < 25)])
age_bins[2] = len(ages[(25 <= ages) & (ages <= 30)])
age_bins[3] = len(ages[(30 < ages) & (ages <= 35)])
age_bins[4] = len(ages[35 < ages])
ax.pie(age_bins,
labels=["18-20", "20-24", "25-30", "31-35", "> 35"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot Python experience
ax = pyplot.subplot(2, 2, 2)
ax.set_title("Years of\nPython Experience",
family=font_family,
size=title_size)
py = exp_df["py_years"]
py_bins = [0, 0, 0, 0, 0]
py_bins[0] = len(py[py < .5])
py_bins[1] = len(py[(.5 <= py) & (py <= 1)])
py_bins[2] = len(py[(1 < py) & (py <= 2)])
py_bins[3] = len(py[(2 < py) & (py <= 5)])
py_bins[4] = len(py[5 < py])
ax.pie(py_bins,
labels=["< 1/2", "1/2-1", "1-2", "2-5", "> 5"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot programming experience
ax = pyplot.subplot(2, 2, 3)
ax.set_title("Years of\nProgramming Experience",
family=font_family,
size=title_size)
prog = exp_df["prog_years"]
prog_bins = [0, 0, 0, 0, 0]
prog_bins[0] = len(prog[prog < 2])
prog_bins[1] = len(prog[(2 <= prog) & (prog <= 3)])
prog_bins[2] = len(prog[(3 < prog) & (prog <= 5)])
prog_bins[3] = len(prog[(5 < prog) & (prog <= 10)])
prog_bins[4] = len(prog[10 < prog])
ax.pie(prog_bins,
labels=["< 2", "2-3", "3-5", "5-10", "> 10"],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
# Bin and plot education
ax = pyplot.subplot(2, 2, 4)
ax.set_title("Highest Degree\nReceived",
family=font_family,
size=title_size)
degrees = exp_df["degree"].value_counts()
ax.pie(degrees.values,
labels=[x.capitalize() for x in degrees.keys()],
autopct="%1.1f%%",
shadow=False,
colors=colors)
shade_axis(ax, size=text_size)
pyplot.tight_layout()
pyplot.savefig("plots/demographics.png")
