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output_path = None, output_safe = True,to_folder = False):
"""""" Prints information about the DataFrame to a file or to the prompt.
Parameters
----------
df - DataFrame
The DataFrame to summarize
preview_rows - int, default 5
Amount of rows to preview from the head and tail of the DataFrame
display_max_cols - int, default None
Maximum amount of columns to display. If set to None, all columns will be displayed.
If set to 0, only as many as fit in the screen's width will be displayed
display_width - int, default None
Width of output. Can be width of file or width of console for printing.
Set to None for pandas to detect it from console.
output_path - path-like, default None
If not None, this will be used as the path of the output file, and this
function will print to a file instead of to the prompt
output_safe - boolean, default True
If True and output_file is not None, this function will not overwrite any
existing files.
output_csv: boolean, default False
If True, will output to a directory with name of output_path with all data in
csv format. WARNING: If set to true, this function will overwrite existing files
in the directory with the following names:
['Preview.csv','Describe.csv','Info.csv','Percentile Details.csv',
'Missing Values Summary.csv','Potential Outliers.csv','Correlation Matrix.csv']
""""""
assert type(df) is pd.DataFrame
# Reformat displays
initial_settings = pd_settings(display_max_cols, None, display_width)
# --------Values of data-----------
df_preview = _io.preview(df,preview_rows)
df_desc_num, df_desc_cat = detailed_desc(df)
percent_values = stats.percentiles(df)
potential_outliers = stats.df_outliers(df).dropna(axis = 1,how = 'all')
potential_outliers = potential_outliers if _utils.rows(potential_outliers) else None
corr_values = regstats.corr_matrix(df)
# ----------Build lists------------
title_list = \
['Preview','Describe (Numerical)','Describe (Categorical)','Percentile Details',
'Potential Outliers','Correlation Matrix']
info_list = \
[df_preview,df_desc_num, df_desc_cat,percent_values,
potential_outliers,corr_values]
error_list = [None,'No numerical data.','All numerical data.','No numerical data.',
'No potential outliers.','No categorical, bool, or numerical data.']
# ----------Build output------------
output = ''
for title, value,error_text in zip(title_list,info_list,error_list):
if value is None:
value = ""{} skipped: {}"".format(title,error_text)
if str(value).endswith('\n'):
value = value[:-1]
output+='{}\n{}\n\n'.format(_io.title_line(title),value)
# ----------Send to file/print to console------------
if output_path is None:
# Potentially could change this to allow for output_safe to work with directories
print(output)
else:
if not to_folder:
print('Outputting to file...')
_io.output_to_file(output,output_path,output_safe)
else:
print('Outputting to folder...')
if not os.path.exists(output_path):
os.mkdir(output_path)
for title, value,error_text in zip(title_list,info_list,error_list):
if value is None:
print(""{} skipped: {}"".format(title,error_text))
else:
file_dir = os.path.join(output_path,""{}.csv"".format(title))
if type(value) is pd.DataFrame:
# Eventually add a check to see if file exists
value.to_csv(file_dir)
else:
_io.output_to_file(value,file_dir,False)
# Change to output_safe when directory output_safe is implemented
print('Done!')
# Reset display settings
pd_settings(*initial_settings)"
4073,"def timed_pipe(generator, seconds=3):
''' This is a time limited pipeline. If you have a infinite pipeline and
want it to stop yielding after a certain amount of time, use this! '''
# grab the highest precision timer
# when it started
start = ts()
# when it will stop
end = start + seconds
# iterate over the pipeline
for i in generator:
# if there is still time