Python scripts from the command line

In this lesson we’ll work with bash command line, instead of the Jupyter notebook. We want to write a python script ‘read.py’ that takes a set of gapminder_gdp_*.csv files (one/few/many) as an argument and prints out various statistic (min, max, mean) for each year for all countries in that file:

$ ./read.py --min data-python/gapminder_gdp_{americas,europe}.csv   # minimum for each year
$ ./read.py --max data-python/gapminder_gdp_europe.csv              # maximum for each year
$ ./read.py --mean data-python/gapminder_gdp_europe.csv data-python/gapminder_gdp_asia.csv

It would be best to open two shells for the following programming, and keep nano always open in one shell.

Put the following into a file called read.py:

#!/usr/bin/env python
import sys
print('argument is', sys.argv)

and then run it from bash:

$ chmod u+x read.py
$ ./read.py     # it'll produce: argument is ['read.py'] (the name of the script is always there)
$ ./read.py one two three     # it'll produce: argument is ['read.py', 'one', 'two', 'three']

Let’s modify our script:

import sys
print(sys.argv[1:])

$ python read.py one two three     # it'll produce ['one', 'two', 'three']

Let’s modify our script:

import sys
for f in sys.argv[1:]:
    print(f)

$ python read.py one two three     # it'll produce one two three (one per line)
$ python read.py data-python/gapminder_gdp_*
$ python read.py data-python/gapminder_gdp_americas.csv data-python/gapminder_gdp_europe.csv
$ python read.py data-python/gapminder_gdp_{americas,europe}.csv     # same as last line

Let’s actually read the data:

import sys
import pandas as pd
for f in sys.argv[1:]:
    print('\n', f[26:-4].capitalize())
    data = pd.read_csv(f, index_col='country')
    print(data.shape, '\n')

$ python read.py data-python/gapminder_gdp_{americas,europe}.csv
$ wc -l !$      # the output should be consistent

Let’s modify our script changing print(data.shape) -> print(data.mean()) to compute average per year for each file:

$ python read.py data-python/gapminder_gdp_{americas,europe}.csv

Let’s add an action flag and calculate statistics based on the flag:

import sys
import pandas as pd
action = sys.argv[1]
for f in sys.argv[2:]:
    print('\n', f[26:-4].capitalize())
    data = pd.read_csv(f, index_col='country')
    if 'continent' in data.columns.tolist():   # if 'continent' column exists
        data = data.drop('continent',1)        #     delete it (1 stands for column, 0 for row)
    if action == '--min':
        values = data.min().tolist()
    elif action == '--mean':
        values = data.mean().tolist()
    elif action == '--max':
        values = data.max().tolist()
    print(values,'\n')

$ python read.py --min data-python/gapminder_gdp_{americas,europe}.csv
$ python read.py --max data-python/gapminder_gdp_{americas,europe}.csv
$ python read.py --mean data-python/gapminder_gdp_{americas,europe}.csv
$ python read.py --mean data-python/gapminder_gdp_*.csv   # should print data for all five continents

Let’s add assertion for action (syntax is: assert n > 0.0, ‘should be positive’) right after the definition of action:

assert action in ['--min', '--mean', '--max'], 'action must be one of: --min --mean --max'

and try passing some other action.

Now let’s package processing of a file (reading + computing + printing) as a function:

import sys
import pandas as pd
action = sys.argv[1]
assert action in ['--min', '--mean', '--max'], 'action must be one of: --min --mean --max'
filenames = sys.argv[2:]
def process(filename, action):
    print('\n', filename[26:-4].capitalize())
    data = pd.read_csv(filename, index_col='country')
    if 'continent' in data.columns.tolist():   # if 'continent' column exists
        data = data.drop('continent',1)        #     delete it (1 stands for column, 0 for row)
    if action == '--min':
        values = data.min().tolist()
    elif action == '--mean':
        values = data.mean().tolist()
    elif action == '--max':
        values = data.max().tolist()
    print(values,'\n')
for f in filenames:
    process(f, action)

Adding standard input support to Python scripts

Python scripts can process standard input. Consider the following script input.py:

#!/usr/bin/env python
import sys
for line in sys.stdin:
    print(line, end='')

In the terminal make it executable (chmod u+x input.py), and then use it to receive any standard input:

./input.py                          # repeat each line you type until Ctrl-C
echo one two three | ./input.py     # print back the line
ls -l | ./input.py                  # list all files
cat input.py | ./input.py           # print itself from standard input
tail -1 input.py | ./input.py       # print its own last line

Let’s add support for Unix standard input to read.py. Delete ‘print(’\n’, f[26:-4].capitalize())’ and change the last two lines to:

if len(filenames) == 0:
    process(sys.stdin,action)       # process standard input
else:
    for f in filenames:             # same as before
        print('\n', f[26:-4].capitalize())
        process(f,action)

$ python read.py --mean data-python/gapminder_gdp_europe.csv
$ python read.py --mean < data-python/gapminder_gdp_europe.csv    # anyone knows why this could be useful?

Why would you want to support standard input? Answer: you can do preprocessing in bash before passing the data to your Python script, e.g.

$ head -5 data-python/gapminder_gdp_europe.csv | python read.py --mean    # process only first five countries
cp data-python/gapminder_gdp_asia.csv a1
cat data-python/gapminder_gdp_europe.csv | sed '1d' >> a1   # add all European data without the header
cat a1 | python read.py --mean          # merge Asian and European data and calculate joint statistics

Here is our full script:

import sys
import pandas as pd
action = sys.argv[1]
assert action in ['--min', '--mean', '--max'], 'action must be one of: --min --mean --max'
filenames = sys.argv[2:]
def process(filename, action):
    data = pd.read_csv(filename, index_col='country')
    if 'continent' in data.columns.tolist():   # if 'continent' column exists
        data = data.drop('continent',1)        #     delete it (1 stands for column, 0 for row)
    if action == '--min':
        values = data.min().tolist()
    elif action == '--mean':
        values = data.mean().tolist()
    elif action == '--max':
        values = data.max().tolist()
    print(values,'\n')
if len(filenames) == 0:
    process(sys.stdin,action)       # process standard input
else:
    for f in filenames:             # same as before
        print('\n', f[26:-4].capitalize())
        process(f,action)