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for k,v in sorted(foo.items()):
    print k, v

for k in sorted(foo.keys()):
    print k, foo[k]
    
    
More at: https://stackoverflow.com/questions/364519/in-python-how-do-i-iterate-over-a-dictionary-in-sorted-key-order

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import json
with open('json_file.json') as f:
  data = json.load(f)
print(data)

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"""
y2k

19016 -> 01/23/2020
0 = 12/31/1967
1 = 01/01/1968
"""

import sys
from datetime import datetime, timedelta

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https://swapi.co/

http://dummy.restapiexample.com/

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curl https://raw.githubusercontent.com/sivel/speedtest-cli/master/speedtest.py | python -

# Silent version
#curl -s https://raw.githubusercontent.com/sivel/speedtest-cli/master/speedtest.py | python -

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p 15count_coverage.py 2>&1 | tee out.txt

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nme = ["aparna", "pankaj", "sudhir", "Geeku"] 
deg = ["MBA", "BCA", "M.Tech", "MBA"] 
scr = [90, 40, 80, 98] 
# New from dictionary 
dict = {'name': nme, 'degree': deg, 'score': scr}  
df = pd.DataFrame(dict) 
clean_df.to_csv(OUTPUT_FILENAME, index=False)


# New dataframe from subset of pandas dataframe

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# Histogram of words per document in the first page

import matplotlib.pyplot as plt
plt.style.use("ggplot")
plt.hist([s for s in words_per_doc], bins=10)
plt.xlabel('Documents')
plt.ylabel('Number of words')
plt.show()

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df = pd.read_csv(FILENAME, names=["id", "title", "text"],
                 escapechar='\\', encoding='utf-8', header=0)

print(df.describe()) # Summary statistics (count, unique, top, freq) for every column of the dataframe
print(df.info()) # Information about each column on the dataframe, and memory usage

print(df.head()) # Show first 5 rows of dataframe
print(df.head(3)) # Show first 3 rows of dataframe

# Head with only a few columns, with random sampling of 10 rows

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#!/usr/bin/python

import subprocess
import re

# Get process info
ps = subprocess.Popen(['ps', '-caxm', '-orss,comm'],
                      stdout=subprocess.PIPE).communicate()[0].decode()
vm = subprocess.Popen(['vm_stat'], stdout=subprocess.PIPE).communicate()[
    0].decode()