def plot_boxplot(column_data=None, output=None, path=None):
"""
Box plot
:param column_data: column data in json format
:param output: image, base64 or plot. Image output a file, base64 output a base64 encoded image and plot output the
image to the notebook
:param path:
:return:
"""
for col_name, stats in column_data.items():
fig, axes = plt.subplots(1, 1)
bp = axes.bxp(stats, patch_artist=True)
axes.set_title(col_name)
plt.figure(figsize=(12, 5))
# 'fliers', 'means', 'medians', 'caps'
for element in ['boxes', 'whiskers']:
plt.setp(bp[element], color='#1f77b4')
for patch in bp['boxes']:
patch.set(facecolor='white')
# Tweak spacing to prevent clipping of tick-labels
# Save as base64
if output is "base64":
return output_base64(fig)
elif output is "image":
output_image(fig, path)
print_html("<img src='" + path + "'>")
def plot_correlation(cols_data, output=None, path=None):
"""
Plot a correlation plot
:param cols_data:
:param output:
:param path:
:return:
"""
import pandas as pd
df = pd.DataFrame(data=cols_data["data"],
columns=cols_data["cols"],
index=cols_data["cols"])
sns_plot = sns.heatmap(df,
mask=np.zeros_like(cols_data["data"],
dtype=np.bool),
cmap=sns.diverging_palette(220, 10, as_cmap=True),
annot=True)
if output is "base64":
# fig = sns.get_figure()
fig = sns_plot.get_figure()
return output_base64(fig)
elif output is "image":
# Save image
fig = sns_plot.get_figure()
fig.savefig(path)
print_html("<img src='" + path + "'>")
def plot_scatterplot(column_data=None, output=None, path=None):
"""
Scatter plot
:param column_data: column data in json format
:param output: image or base64
:param path:
:return:
"""
fig = plt.figure(figsize=(12, 5))
plt.scatter(column_data["x"]["data"],
column_data["y"]["data"],
s=column_data["s"],
alpha=0.5)
plt.xlabel(column_data["x"]["name"])
plt.ylabel(column_data["y"]["name"])
if output is "base64":
return output_base64(fig)
elif output is "image":
output_image(fig, path)
print_html("<img src='" + path + "'>")
elif output is "plot":
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.3)
def plot_hist(column_data=None, output=None, sub_title="", path=None):
"""
Plot a histogram
obj = {"col_name":[{'lower': -87.36666870117188, 'upper': -70.51333465576172, 'value': 0},
{'lower': -70.51333465576172, 'upper': -53.66000061035157, 'value': 22094},
{'lower': -53.66000061035157, 'upper': -36.80666656494141, 'value': 2},
...
]}
:param column_data: column data in json format
:param output: image, base64 or plot. Image output a file, base64 output a base64 encoded image and plot output the
image to the notebook
:param sub_title: plot subtitle
:param path:
:return: plot, image or base64
"""
for col_name, data in column_data.items():
bins = []
# print(data)
# print("**********")
for d in data:
bins.append(d['lower'])
last = data[len(data) - 1]["upper"]
bins.append(last)
# Transform hist Optimus format to matplot lib format
hist = []
for d in data:
if d is not None:
hist.append(d["count"])
array_bins = array(bins)
center = (array_bins[:-1] + array_bins[1:]) / 2
width = 0.9 * (array_bins[1] - array_bins[0])
hist = one_list_to_val(hist)
# Plot
fig = plt.figure(figsize=(12, 5))
plt.bar(center, hist, width=width)
plt.title("Histogram '" + col_name + "' " + sub_title)
# fig.tight_layout()
if output is "base64":
return output_base64(fig)
elif output is "image":
# Save image
output_image(plt, path)
print_html("<img src='" + path + "'>")
# Print in jupyter notebook
elif output is "plot":
plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.3)
def plot_frequency(column_data=None, output=None, path=None):
"""
Frequency plot
:param column_data: column data in json format
:param output: image, base64 or plot. Image output a file, base64 output a base64 encoded image and plot output the
image to the notebook
:param path:
:return:
"""
for col_name, data in column_data.items():
# Transform Optimus' format to matplotlib's format
x = []
h = []
for d in data:
x.append(ellipsis(d["value"]))
h.append(d["count"])
# Plot
fig = plt.figure(figsize=(12, 5))
# Need to to this to plot string labels on x
x_i = range(len(x))
plt.bar(x_i, h)
plt.xticks(x_i, x)
plt.title("Frequency '" + col_name + "'")
plt.xticks(rotation=45, ha="right")
plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.3)
if output is "base64":
return output_base64(fig)
elif output is "image":
output_image(plt, path)
print_html("<img src='" + path + "'>")
elif output is "plot":
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.3)
def plot_qqplot(col_name, sample_data, output="plot", path=None):
"""
Plot a qqplot
:param col_name:
:param sample_data:
:param output:
:param path:
:return:
"""
fig = plt.figure(figsize=(12, 5))
sm.qqplot(sample_data.toPandas()[col_name],
line='q',
color='C0',
alpha=0.3)
plt.title("qqplot '" + col_name + "' ")
if output is "base64":
return output_base64(fig)
elif output is "image":
output_image(plt, path)
def plot_missing_values(column_data=None, output=None, path=None):
"""
Plot missing values
:param column_data:
:param output: image, base64 or plot. Image output a file, base64 output a base64 encoded image and plot output the
:param path:
image to the notebook
:return:
"""
values = []
columns = []
labels = []
for col_name, data in column_data["data"].items():
values.append(data["missing"])
columns.append(col_name)
labels.append(data["%"])
# Plot
fig = plt.figure(figsize=(12, 5))
plt.bar(columns, values)
plt.xticks(columns, columns)
# Highest limit
highest = column_data["count"]
plt.ylim(0, 1.05 * highest)
plt.title("Missing Values")
i = 0
for label, val in zip(labels, values):
plt.text(x=i - 0.5,
y=val + (highest * 0.05),
s="{}({})".format(val, label))
i = i + 1
if output is "base64":
return output_base64(fig)
elif output is "image":
output_image(plt, path)
elif output is "plot":
plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.3)