def frequency(columns=None, buckets=10, output_format="plot", output_path=None):
"""
Plot frequency chart
:param columns: Columns to be printed
:param buckets: Number of buckets
:param output_format:
:param output_path: path where the image is going to be saved
:return:
"""
columns = parse_columns(self, columns)
data = self.cols.frequency(columns, buckets)
for k, v in data.items():
plot_frequency({k: v}, output=output_format, path=output_path)
def run(self,
df,
columns="*",
buckets=MAX_BUCKETS,
infer=False,
relative_error=RELATIVE_ERROR,
approx_count=True,
mismatch=None):
"""
Return dataframe statistical information in HTML Format
:param df: Dataframe to be analyzed
:param columns: Columns to be analyzed
:param buckets: Number of buckets calculated to print the histogram
:param infer: infer data type
:param relative_error: Relative Error for quantile discretizer calculation
:param approx_count: Use approx_count_distinct or countDistinct
:param mismatch:
:return:
"""
columns = parse_columns(df, columns)
columns, output = self.dataset(df,
columns,
buckets,
infer,
relative_error,
approx_count,
format="dict",
mismatch=mismatch)
# Load jinja
template_loader = jinja2.FileSystemLoader(
searchpath=absolute_path("/profiler/templates/out"))
template_env = jinja2.Environment(loader=template_loader,
autoescape=True)
# Render template
# Create the profiler info header
html = ""
general_template = template_env.get_template("general_info.html")
html = html + general_template.render(data=output)
template = template_env.get_template("one_column.html")
# Create every column stats
for col_name in columns:
hist_pic = None
freq_pic = None
col = output["columns"][col_name]
if "hist" in col["stats"]:
hist_dict = col["stats"]["hist"]
if col["column_dtype"] == "date":
hist_year = plot_hist({col_name: hist_dict["years"]},
"base64", "years")
hist_month = plot_hist({col_name: hist_dict["months"]},
"base64", "months")
hist_weekday = plot_hist({col_name: hist_dict["weekdays"]},
"base64", "weekdays")
hist_hour = plot_hist({col_name: hist_dict["hours"]},
"base64", "hours")
hist_minute = plot_hist({col_name: hist_dict["minutes"]},
"base64", "minutes")
hist_pic = {
"hist_years": hist_year,
"hist_months": hist_month,
"hist_weekdays": hist_weekday,
"hist_hours": hist_hour,
"hist_minutes": hist_minute
}
elif col["column_dtype"] == "int" or col[
"column_dtype"] == "string" or col[
"column_dtype"] == "decimal":
hist = plot_hist({col_name: hist_dict}, output="base64")
hist_pic = {"hist_numeric_string": hist}
if "frequency" in col:
freq_pic = plot_frequency({col_name: col["frequency"]},
output="base64")
html = html + template.render(
data=col, freq_pic=freq_pic, hist_pic=hist_pic)
# Save in case we want to output to a html file
# self.html = html + df.table_html(10)
self.html = html
# Display HTML
print_html(self.html)
# JSON
# Save in case we want to output to a json file
self.json = output
return self
def run(self,
df,
columns,
buckets=40,
infer=False,
relative_error=1,
approx_count=True):
"""
Return dataframe statistical information in HTML Format
:param df: Dataframe to be analyzed
:param columns: Columns to be analyzed
:param buckets: Number of buckets calculated to print the histogram
:param infer: infer data type
:param relative_error: Relative Error for quantile discretizer calculation
:param approx_count: Use approx_count_distinct or countDistinct
:return:
"""
columns = parse_columns(df, columns)
output = Profiler.to_json(df, columns, buckets, infer, relative_error,
approx_count)
# Load jinja
path = os.path.dirname(os.path.abspath(__file__))
template_loader = jinja2.FileSystemLoader(searchpath=path +
"//templates")
template_env = jinja2.Environment(loader=template_loader,
autoescape=True)
# Render template
# Create the profiler info header
html = ""
general_template = template_env.get_template("general_info.html")
html = html + general_template.render(data=output)
template = template_env.get_template("one_column.html")
# Create every column stats
for col_name in columns:
hist_pic = None
col = output["columns"][col_name]
if "hist" in col:
if col["column_dtype"] == "date":
hist_year = plot_hist({col_name: col["hist"]["years"]},
"base64", "years")
hist_month = plot_hist({col_name: col["hist"]["months"]},
"base64", "months")
hist_weekday = plot_hist(
{col_name: col["hist"]["weekdays"]}, "base64",
"weekdays")
hist_hour = plot_hist({col_name: col["hist"]["hours"]},
"base64", "hours")
hist_minute = plot_hist({col_name: col["hist"]["minutes"]},
"base64", "minutes")
hist_pic = {
"hist_years": hist_year,
"hist_months": hist_month,
"hist_weekdays": hist_weekday,
"hist_hours": hist_hour,
"hist_minutes": hist_minute
}
else:
hist = plot_hist({col_name: col["hist"]}, output="base64")
hist_pic = {"hist_pic": hist}
if "frequency" in col:
freq_pic = plot_frequency({col_name: col["frequency"]},
output="base64")
else:
freq_pic = None
html = html + template.render(
data=col, freq_pic=freq_pic, **hist_pic)
html = html + df.table_html(10)
# Display HTML
print_html(html)
# send to queue
if self.queue_url is not None:
self.to_queue(output)
# JSON
# Save in case we want to output to a json file
self.json = output
# Save file in json format
write_json(output, self.path)
# Save in case we want to output to a html file
self.html = html
