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ph - the tabular data shell tool

Project description

ph (pronounced φ) - the tabular data shell tool ph tests

Spoiler: Working with tabular data in the command line is difficult. ph makes it easy:

$ pip install ph
$ cat iris.csv | ph columns
150
4
setosa
versicolor
virginica
$ cat iris.csv | ph columns setosa versicolor | ph head 15 | ph tail 5 | ph show
      setosa    versicolor
--  --------  ------------
 0       1.5           0.2
 1       1.6           0.2
 2       1.4           0.1
 3       1.1           0.1
 4       1.2           0.2
$ cat iris.csv | ph describe
              150           4      setosa  versicolor   virginica
count  150.000000  150.000000  150.000000  150.000000  150.000000
mean     5.843333    3.057333    3.758000    1.199333    1.000000
std      0.828066    0.435866    1.765298    0.762238    0.819232
min      4.300000    2.000000    1.000000    0.100000    0.000000
25%      5.100000    2.800000    1.600000    0.300000    0.000000
50%      5.800000    3.000000    4.350000    1.300000    1.000000
75%      6.400000    3.300000    5.100000    1.800000    2.000000
max      7.900000    4.400000    6.900000    2.500000    2.000000

Occasionally you would like to plot a CSV file real quick, in which case you can simply pipe it to ph plot:

Suppose you have a dataset covid.csv

SK,Italy,Iran,France,Spain,US
51,79,95,57,84,85
104,150,139,100,125,111
204,227,245,130,169,176
433,320,388,191,228,252
602,445,593,212,282,352
833,650,978,285,365,495
977,888,1501,423,430,640
1261,1128,2336,613,674,926
1766,1694,2922,949,1231,NaN
2337,2036,3513,1126,1696,NaN
3150,2502,4747,1412,NaN,NaN
4212,3089,5823,1748,NaN,NaN
4812,3858,6566,NaN,NaN,NaN
5328,4638,7161,NaN,NaN,NaN
5766,5883,8042,NaN,NaN,NaN
6284,7375,NaN,NaN,NaN,NaN
6767,9172,NaN,NaN,NaN,NaN
7134,10149,NaN,NaN,NaN,NaN
7382,NaN,NaN,NaN,NaN,NaN
7513,NaN,NaN,NaN,NaN,NaN

With this simple command, you get a certified "So fancy" plot.

$ cat covid.csv | ph plot

So fancy covid plot

(Notice that this needs matplotlib: pip install ph[plot])


Raison d'être

Using the pipeline in Linux is nothing short of a dream in the life of the computer super user.

However the pipe is clearly most suited for a stream of lines of textual data, and not when the stream is actually tabular data.

Tabular data is much more complex to work with due to its dual indexing and the fact that we often read horizontally and often read vertically.

The defacto format for tabular data is csv (which is not perfect in any sense of the word), and the defacto tool for working with tabular data in Python is Pandas.

This is a shell utility ph that reads tabular data from standard in and allows you to perform a pandas function on the data, before writing it to standard out in csv format.

The goal is to create a tool which makes it nicer to work with tabular data in a pipeline.



Getting started

If you have installed ph[data], you can experiment using ph dataset if you don't have an appropriate csv file available.

ph dataset boston | ph describe

Available datasets are from scikit-learn.datasets

Toy datasets:

  • boston
  • iris
  • diabetes
  • digits
  • linnerud
  • wine
  • breast_cancer

Real world:

  • olivetti_faces
  • lfw_people
  • lfw_pairs
  • rcv1
  • kddcup99
  • california_housing

Example usage

Suppose you have a csv file a.csv that looks like this:

x,y
3,8
4,9
5,10
6,11
7,12
8,13

Transpose:

$ cat a.csv | ph transpose
0,1,2,3,4,5
3,4,5,6,7,8
8,9,10,11,12,13

median (as well as many others, e.g. abs, corr, count, cov, cummax, cumsum, diff, max, product, quantile, rank, round, sum, std, var etc.):

$ cat a.csv | ph median
0
5.5
10.5

Use ph help to list all commands

The tools

Concatenating, merging, filtering

cat

It is possible to concatenate (cat) multiple csv-files with ph cat:

$ ph cat a.csv b.csv --axis=index
$ ph cat a.csv b.csv --axis=columns

The functionality is described in pandas.concat.

dropna and fillna

Consider again the covid.csv file from above.

$ cat covid.csv | ph dropna

will remove all rows that contain N/A (nan) values. If we want to keep all rows with at least 5 non-N/A values, we can use

$ cat covid.csv | ph dropna --thresh=5

If we want to drop all columns with N/A values instead of all rows, we use --axis=1.

If we want to drop only columns (resp. rows) with all n/a values, we use --how=all.

To replace N/A values with other values, we can simply run

cat covid.csv | ph fillna 999.75

If we instead want to pad the N/A values, we use --method=pad

cat covid.csv | ph fillna --method=pad

We can limit the number of consecutive N/A values that are filled by using (e.g.) --limit=7.

head and tail

Using head and tail works approximately as the normal shell equivalents, however they will preserve the header if there is one, e.g.

$ cat a.csv | ph head 7 | ph tail 3
x,y
6,11
7,12
8,13

If the csv file contains a column, e.g. named x containing timestamps, it can be parsed as such with ph date x:

$ cat a.csv | ph date x
x,y
1970-01-04,8
1970-01-05,9
1970-01-06,10
1970-01-07,11
1970-01-08,12
1970-01-09,13

If your column is formatted with freedom units, mm/dd/yyyy, you can use the flag --dayfirst=True:

dateRep,geoId
01/04/2020,US
31/03/2020,US
30/03/2020,US
29/03/2020,US
28/03/2020,US
$ cat ~/cov.csv | ph date dateRep --dayfirst=True
dateRep,geoId
2020-04-01,US
2020-03-31,US
2020-03-30,US
2020-03-29,US
2020-03-28,US

To get a column with integers (e.g. 3-8) parsed as, e.g. 2003 - 2008, some amount of hacking is necessary. We will go into details later on the eval and appendstr.

$ cat a.csv | ph eval "x = 2000 + x" | ph appendstr x - | ph date x
x,y
2003-01-01,8
2004-01-01,9
2005-01-01,10
2006-01-01,11
2007-01-01,12
2008-01-01,13

merge

Merging two csv files is made available through ph merge f1 f2.

Consider left.csv

key1,key2,A,B
K0,K0,A0,B0
K0,K1,A1,B1
K1,K0,A2,B2
K2,K1,A3,B3

and right.csv

key1,key2,C,D
K0,K0,C0,D0
K1,K0,C1,D1
K1,K0,C2,D2
K2,K0,C3,D3

We can merge them using (default to --how=inner)

$ ph merge left.csv right.csv
key1,key2,A,B,C,D
K0,K0,A0,B0,C0,D0
K1,K0,A2,B2,C1,D1
K1,K0,A2,B2,C2,D2

or using an outer join:

$ ph merge left.csv right.csv --how=outer
key1,key2,A,B,C,D
K0,K0,A0,B0,C0,D0
K0,K1,A1,B1,,
K1,K0,A2,B2,C1,D1
K1,K0,A2,B2,C2,D2
K2,K1,A3,B3,,
K2,K0,,,C3,D3

and we can specify on which column to join:

$ ph merge left.csv right.csv --on=key1 --how=outer
key1,key2_x,A,B,key2_y,C,D
K0,K0,A0,B0,K0,C0,D0
K0,K1,A1,B1,K0,C0,D0
K1,K0,A2,B2,K0,C1,D1
K1,K0,A2,B2,K0,C2,D2
K2,K1,A3,B3,K0,C3,D3

Editing the csv

columns, listing, selecting and re-ordering of

Consider c.csv:

it,fr,de
79,57,79
157,100,130
229,130,165
323,191,203
470,212,262
655,285,545
889,423,670
1128,653,800
1701,949,1040
2036,1209,1224
2502,1412,1565
3089,1784,1966
3858,2281,2745
4636,2876,3675
5883,3661,4181

Print the column names:

$ cat c.csv | ph columns
it
fr
de

Selecting only certain columns, e.g. de and it

$ cat c.csv | ph columns de it | ph tail 3
de,it
2745,3858
3675,4636
4181,5883

rename

$ cat c.csv | ph rename de Germany | ph rename it Italy | ph columns Italy Germany
Italy,Germany
79,79
157,130
229,165
323,203
470,262
655,545
889,670
1128,800
1701,1040
2036,1224
2502,1565
3089,1966
3858,2745
4636,3675
5883,4181

In addition to rename there is an auxiliary function slugify that lets you slugify the column names. Consider slugit.csv

  Stupid column 1,  Jerky-column No. 2
3,8
4,9
5,10
6,11
7,12
8,13
$ cat slugit.csv | ph slugify
stupid_column_1,jerky_column_no_2
3,8
4,9
5,10
6,11
7,12
8,13

Then you can do

$ cat slugit.csv | ph slugify | ph rename stupid_column_1 first | ph rename jerky_column_no_2 second
first,second
3,8
4,9
5,10
6,11
7,12
8,13

eval; Mathematipulating and creating new columns

You can sum columns and place the result in a new column using eval (from pandas.DataFrame.eval).

$ cat c.csv | ph eval "total = it + fr + de" | ph tail 3
it,fr,de,total
3858,2281,2745,8884
4636,2876,3675,11187
5883,3661,4181,13725
$ cat a.csv | ph eval "z = x**2 + y"
x,y,z
3,8,17
4,9,25
5,10,35
6,11,47
7,12,61
8,13,77

If you only want the result, you leave the eval expression without assignment

$ cat a.csv | ph eval "x**2"
x
9
16
25
36
49
64

normalize

You can normalize a column using ph normalize col.

$ cat a.csv | ph eval "z = x * y" | ph normalize z
x,y,z
3,8,0.0
4,9,0.15
5,10,0.325
6,11,0.525
7,12,0.75
8,13,1.0

query

We can query data using ph query expr.

$ cat a.csv | ph query "x > 5"
x,y
6,11
7,12
8,13

Warning: Calling the following command might be illegal in Norway. Reader beware!

$ ph open csv 'http://bit.ly/2cLzoxH' | ph query "country == 'Norway'" | ph tabulate --headers
    country      year          pop  continent      lifeExp    gdpPercap
--  ---------  ------  -----------  -----------  ---------  -----------
 0  Norway       1952  3.32773e+06  Europe          72.67       10095.4
 1  Norway       1957  3.49194e+06  Europe          73.44       11654
 2  Norway       1962  3.63892e+06  Europe          73.47       13450.4
 3  Norway       1967  3.78602e+06  Europe          74.08       16361.9
 4  Norway       1972  3.933e+06    Europe          74.34       18965.1
 5  Norway       1977  4.04320e+06  Europe          75.37       23311.3
 6  Norway       1982  4.11479e+06  Europe          75.97       26298.6
 7  Norway       1987  4.18615e+06  Europe          75.89       31541
 8  Norway       1992  4.28636e+06  Europe          77.32       33965.7
 9  Norway       1997  4.40567e+06  Europe          78.32       41283.2
10  Norway       2002  4.53559e+06  Europe          79.05       44684
11  Norway       2007  4.62793e+06  Europe          80.196      49357.2

Analyzing the csv file

describe

The normal Pandas describe is of course available:

$ cat a.csv | ph describe
              x          y
count  6.000000   6.000000
mean   5.500000  10.500000
std    1.870829   1.870829
min    3.000000   8.000000
25%    4.250000   9.250000
50%    5.500000  10.500000
75%    6.750000  11.750000
max    8.000000  13.000000

show

The shorthand ph show simply calls the below ph tabulate --headers.

$ cat a.csv | ph show
      x    y
--  ---  ---
 0    3    8
 1    4    9
 2    5   10
 3    6   11
 4    7   12
 5    8   13

tabulate

The amazing tabulate tool comes from the Python package tabulate on PyPI.

The tabulate command takes arguments --headers to toggle printing of header row, --format=[grid,...] to modify the table style and --noindex to remove the running index (leftmost column in the example above).

Among the supported format styles are

  • plain, simple,
  • grid, fancy_grid, pretty,
  • github, rst, mediawiki, html, latex,
  • ... (See full list at the project homepage at python-tabulate.)

sort values by column

You can the columns in the csv data by a certain column:

$ cat iris.csv  | ph sort setosa | ph tail 5
150,4,setosa,versicolor,virginica
7.9,3.8,6.4,2.0,2
7.6,3.0,6.6,2.1,2
7.7,3.8,6.7,2.2,2
7.7,2.8,6.7,2.0,2
7.7,2.6,6.9,2.3,2

plot

You can plot data using ph plot [--index=col].

$ ph open parquet 1A_2019.parquet | ph columns Time Value | ph plot --index=Time

This will take the columns Time and Value from the timeseries provided by the given parquet file and plot the Value series using Time as index.

The following example plots the life expectancy in Norway using year as index:

$ ph open csv http://bit.ly/2cLzoxH  | ph query "country == 'Norway'" | ph appendstr year -01-01 | ph columns year lifeExp | ph plot --index=year

life-expectancy over time

Note: The strange ph appendstr year -01-01 turns the items 1956 into "1956-01-01" and 2005 into "2005-01-01". These are necessary to make pandas to interpret 1956 as a year and not as a millisecond.

The command ph appendstr col str [newcol] takes a string and appends it to a column, overwriting the original column, or writing it to newcol if provided.

Advanced plotting

You can choose the kind of plotting ( ‘line’, ‘bar’, ‘barh’, ‘hist’, ‘box’, ‘kde’, ‘density’, ‘area’, ‘pie’, ‘scatter’, ‘hexbin’), the style of plotting (e.g. --style=o), and in case of scatter plot, you need to specify --x=col1 and --y=col2, e.g.:

$ ph open csv http://bit.ly/2cLzoxH | ph query "continent == 'Europe'" | ph plot --kind=scatter --x=lifeExp --y=gdpPercap

life-expectancy vs gdp

To specify the styling k-- gives a black dashed line:

$ ph open csv http://bit.ly/2cLzoxH  | ph query "country == 'Norway'" | ph appendstr year -01-01 | ph columns year lifeExp | ph plot --index=year --style=k--

iplot with plotly and cufflinks

Instead of using the matplotlib backend, there is an option for using plotly and cufflinks to generate interactive plots. This depends on cufflinks, and can be installed with pip install ph[iplot].

$ cat a.csv | ph iplot --kind=bar --barmode=stack

groupby

Suppose you have a csv file

Animal,Max Speed
Falcon,380.0
Falcon,370.0
Parrot,24.0
Parrot,26.0

You can use Pandas' groupby functionality to get the aggregated sum, mean, or first value:

$ cat group.csv | ph groupby Animal --how=mean
Max Speed
375.0
25.0

If you want to retain the index column,

$ cat group.csv | ph groupby Animal --how=mean --as_index=False
Animal,Max Speed
Falcon,375.0
Parrot,25.0

index

Occasionally you need to have an index, in which case ph index is your tool:

$ cat a.csv | ph index
index,x,y
0,3,8
1,4,9
2,5,10
3,6,11
4,7,12
5,8,13

polyfit

You can perform linear regression and polynomial regression on a certain index column x and a y = f(x) column using ph polyfit. It takes two arguments, the x column name, the y column name and an optional --deg=<degree>, the degree of the polynomial. The default option is --deg=1 which corresponds to a linear regression.

Suppose you have a csv file lr.csv with content

x,y
4,12
5,19
6,17
7,24
8,28
9,34

With linear (polynomial) regression, you get an extra column, polyfit_{deg}:

$ cat lr.csv | ph polyfit x y | ph astype int
x,y,polyfit_1
4,12,12
5,19,16
6,17,20
7,24,24
8,28,28
9,34,32

Using ph plot --index=x results in this plot:

polyfit

Working with different formats

open

Pandas supports reading a multitude of readers.

To read an Excel file and pipe the stream, you can use ph open.

The syntax of ph open is ph open ftype fname, where fname is the file you want to stream and ftype is the type of the file.

A list of all available formats is given below.

$ ph open xls a.xlsx
x,y
3,8
4,9
5,10
6,11
7,12
8,13

You can open a semicolon separated values file using --sep=";"

$ ph open csv --sep=";" fname.csv

to and from; Exporting and importing

Observe the following:

{"x":{"0":3,"1":4,"2":5,"3":6,"4":7,"5":8},
 "y":{"0":8,"1":9,"2":10,"3":11,"4":12,"5":13}}

Of course, then,

$ cat a.csv | ph to json | ph from json
x,y
3,8
4,9
5,10
6,11
7,12
8,13

This also means that

$ cat a.csv | ph to json > a.json
$ cat a.json
{"x":{"0":3,"1":4,"2":5,"3":6,"4":7,"5":8},
 "y":{"0":8,"1":9,"2":10,"3":11,"4":12,"5":13}}
$ cat a.json | ph from json
x,y
3,8
4,9
5,10
6,11
7,12
8,13

You can open Excel-like formats using ph open excel fname.xls[x], parquet files with ph open parquet data.parquet. Note that these two examples require xlrd and pyarrow, respectively, or simply

pip install ph[complete]

Supported formats

  • csv / tsv (the latter for tab-separated values)
  • fwf (fixed-width file format)
  • json
  • html
  • clipboard (pastes tab-separated content from clipboard)
  • xls
  • odf
  • hdf5
  • feather
  • parquet
  • orc
  • stata
  • sas
  • spss
  • pickle
  • sql
  • gbq / google / bigquery

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