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describe.py
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describe.py
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#!/usr/local/bin/python
"""
Copyright (c) 2013 Josh Attenberg
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from scipy.stats import stats, normaltest, sem, bayes_mvs
import sys
from optparse import OptionParser
import numpy as np
import pandas as pd
from math import log
def get_nonnumeric(column, np_values):
data = np_values.describe();
output = [ present("Column", column) ]
for id in data.index:
output.append(present(id.capitalize(), data[id]))
return output
def get_data(column, np_values, alpha):
mvs = bayes_mvs(np_values, alpha)
#report these metrics
output = [
present("Column", column),
present("Length", len(np_values)),
present("Unique", len(np.unique(np_values))),
present("Min", np_values.min()),
present("Max", np_values.max()),
present("Mid-Range", (np_values.max() - np_values.min())/2),
present("Range", np_values.max() - np_values.min()),
present("Mean", np_values.mean()),
present("Mean-%s-CI" % alpha, tupleToString(mvs[0][1])),
present("Variance", mvs[1][0]),
present("Var-%s-CI" % alpha, tupleToString(mvs[1][1])),
present("StdDev", mvs[2][0]),
present("Std-%s-CI" % alpha, tupleToString(mvs[2][1])),
present("Mode", stats.mode(np_values)[0][0]),
present("Q1", stats.scoreatpercentile(np_values, 25)),
present("Q2", stats.scoreatpercentile(np_values, 50)),
present("Q3", stats.scoreatpercentile(np_values, 75)),
present("Trimean", trimean(np_values)),
present("Minhinge", midhinge(np_values)),
present("Skewness", stats.skew(np_values)),
present("Kurtosis", stats.kurtosis(np_values)),
present("StdErr", sem(np_values)),
present("Normal-P-value", normaltest(np_values)[1])
]
return output
#this prints a bit nicer than tuples standard toString
def tupleToString(tuple):
return "(%s, %s)" % tuple
def present(key, value):
return key + " : " + str(value)
def trimean(values):
return (stats.scoreatpercentile(values, 25) + 2.0*stats.scoreatpercentile(values, 50) + stats.scoreatpercentile(values, 75))/4.0
def midhinge(values):
return (stats.scoreatpercentile(values, 25) + stats.scoreatpercentile(values, 75))/2.0
def relations(df, num_columns, output):
output.append( "\nCovariances:" )
output.append( str(df[num_columns].cov()) )
output.append( "\nCorrelations:" )
output.append( str(df[num_columns].corr()) )
def gather_descriptions(df, description):
num_columns = []
for column in df.columns:
#only consider numeric columns
if df[column].dtype.kind == 'i' or df[column].dtype.kind == 'f':
output = get_data(column, df[column], float(options.alpha))
num_columns.append(column)
description.append("\n".join(output))
else:
output = get_nonnumeric(column, df[column])
description.append("\n".join(output))
if not options.simple and df.shape[1] > 1 and len(num_columns) > 1:
relations(df, num_columns, description)
parser = OptionParser(usage="""presents a range of standard descriptive statistics
on columns of numerical data
perl -e 'for($i = 0; $i < 20; $i++){print rand(), "\\t", rand(), "\\t", rand(), "\\n"}' | python describe.py
Usage %prog [options]
""")
parser.add_option('-f', '--file',
action = 'store', dest = 'filename', default=False,
help="[optional] use a specified file instead of reading from stdin")
parser.add_option('-o', '--out',
action = 'store', dest = 'out', default=False,
help="[optional] write to a specified file instead of stdout")
parser.add_option('-H', '--header',
action = 'store_true', dest = 'header', default = None,
help="treat the first row as column headers")
parser.add_option('-d', '--delim',
action='store', dest='delim', default="\t",
help="delimiter seperating columns in input")
parser.add_option('-a', '--alpha',
action='store', dest='alpha', default=0.9,
help="confidence value used in interval estimation")
parser.add_option('-s', '--simple',
action = 'store_true', dest = 'simple', default = False,
help="abbreviated, simplified output")
(options, args) = parser.parse_args()
input = open(options.filename, 'r') if options.filename else sys.stdin
out = open(options.out, 'r') if options.out else sys.stdout
df = pd.read_csv(input, sep = options.delim,
header = 0 if options.header else None)
description = []
gather_descriptions(df, description)
out.write( "\n\n".join(description) + "\n")
out.flush()
out.close()
input.close()