Command line data tools

pandas, plot, plotting, data, dataframe, command, line, cli, statistics, stats
pip install pandashells==0.3.0




For decades, system administrators, dev-ops engineers and data analysts have been piping textual data between unix tools like grep, awk, sed, etc. Chaining these tools together provides an extremely powerful workflow.

The more recent emergence of the "data-scientist" has resulted in the increasing popularity of tools like R, Pandas, IPython, etc. These tools have amazing power for transforming, analyzing and visualizing data-sets in ways that grep, awk, sed, and even the dreaded perl-one-liner could never accomplish.

Pandashells is an attempt to marry the expressive, concise workflow of the shell pipeline with the statistical and visualization tools of the python data-stack.

What is Pandashells?

  • A set of command-line tools for working with tabular data
  • Easily read/write data in CSV, or space delimited formats
  • Quickly aggregate, join, and summarize tabular data
  • Compute descriptive statistics
  • Perform spectral decomposition and linear regression
  • Create data visualizations that can be saved to images or rendered interactively using either a native backend or html.
  • Easily integrate with unix tools like curl, awk, grep, sed, etc.

If you work with data using Python, you have almost certainly encountered Pandas, SciPy, Matplotlib, Statsmodels and Seaborn. Pandashells opens up a bash API into the python data stack with command syntax closely mirroring the underlying libraries on which it is built. This should allow those familiar with the python data stack to be immediately productive.


Install with pip

Pandashells can be installed with pip, but a few words of caution are in order. First, you should really use a recent version of pip so you can access wheels on pypi pip install -U pip. Secondly, if your setup requires building from source rather than using wheels, you may run into problems with systems libraries being either out of date or missing. If you encounter these problems, you may want to use conda to install those dependencies.

Pandashells, no dependencies

Use this option if you want to completely manage your own dependencies. (See requirements section below).

 [~]$ pip install pandashells 

Pandashells console tools

Use this option to install Pandashells and only the console tools dependencies. This will not install the graphics dependencies (matplotlib and friends)

[~]$ pip install pandashells[console]

Pandashells full install

Use this option to install Pandashells and all dependencies

[~]$ pip install pandashells[full]


Pandashells is both Python2 and Python3 compatible. There are no default requirements because some of the tools only require the standard library, and there's no sense installing unnecessary packages if you only want to use that subset of tools. If a particular tool encounters a missing dependency, it will fail with an error message indicating the missing dependency. Below is a list of all imports used across the Pandashells toolkit, and ordered according to install option.

  • [console] numpy, scipy, pandas, statsmodels, gatspy, supersmoother
  • [full] numpy, scipy, pandas, statsmodels, gatspy, supersmoother, matplotlib, mpld3, seaborn

Important: If you want to use pandashells without interactive visualizations (e. g. on a VM without X-forwarding), but would like to retain the ability to create static-image or html-based visualizations, you may need to configure pandashells to use the Agg backend as follows:

p.config --plot_backend Agg


All Pandashells executables begin with a "p." This is designed to work nicely with the bash-completion feature. If you can't remember the exact name of a command, simply typing p.[tab] will show you a complete list of all Pandashells commands.

Every command can be run with a -h option to view help. Each of these help messages will contain multiple examples of how to properly use the tool.

Pandashells is equipped with a tool to generate sample csv files. This tool provides standardized inputs for use in the tool help sections as well as this documentation.

[~]$ p.example_data -h

Tool Descriptions

Tool pip install Purpose
p.config pandashells Set default Pandashells configuration options
p.crypt pandashells Encrypt/decrypt files using open-ssl
p.format pandashells Render python string templates using input data
p.gui pandashells Open dataframe in pandasgui interactive environment
p.parallel pandashells Read shell commands from stdin and run them in parallel
p.example_data pandashells Create sample csv files for training/testing
p.df pandashells[console] Pandas dataframe manipulation of text files
p.linspace pandashells[console] Generate a linearly spaced series of numbers
p.lomb_scargle pandashells[console] Generate Lomb-Scarge spectrogram of input time series
p.merge pandashells[console] Merge two data files by specifying join keys
p.rand pandashells[console] Generate random numbers
p.regress pandashells[console] Perform (multi-variate) linear regression with R-like patsy syntax
p.sig_edit pandashells[console] Remove outliers using iterative sigma-editing
p.smooth pandashells[console] Smooth data
p.cdf pandashells[full] Plot emperical distribution function
p.facet_grid pandashells[full] Create faceted plots for data exploration
p.hist pandashells[full] Plot histograms
p.plot pandashells[full] Create xy plot visualizations
p.regplot pandashells[full] Quickly plot linear regression of data to a polynomial

DataFrame Manipulations

Pandashells allows you to specify multiple dataframe operations in a single command. Each operation assumes data is in a dataframe named df. Operations performed on this dataframe will overwrite the df variable with the results of that operation. Special consideration is taken for assignments such as df['a'] = df.b + 1. These are understood to make column assignments on df. By way of example, this command at the bash prompt:

p.df 'df["c"] = 2 * df.b' 'df.groupby(by="a").c.count()' 'df.reset_index()'

is equivalent to the following python snippet:

import pandas as pd
df = pd.read_csv(sys.stdin)
df["c"] = 2 * df.b
df = df.groupby(by="a").c.count()
df = df.reset_index()
df.to_csv(sys.stdout, index=False)

Shown below are several examples of how to use the p.df tool. You are encourage to copy/paste these commands to your bash prompt to see Pandashells in action.

  • Show a few rows of an example data set.

    [~]$ p.example_data -d tips | head
  • Transform the sample data from csv format to table format

    [~]$ p.example_data -d tips | p.df 'df.head()' -o table
    total_bill   tip     sex smoker  day    time  size
         16.99  1.01  Female     No  Sun  Dinner     2
         10.34  1.66    Male     No  Sun  Dinner     3
         21.01  3.50    Male     No  Sun  Dinner     3
         23.68  3.31    Male     No  Sun  Dinner     2
         24.59  3.61  Female     No  Sun  Dinner     4

  • Compute statistics for numerical fields in the data set.

    [~]$ p.example_data -d tips | p.df 'df.describe().T' -o table index 
                count       mean       std   min      25%     50%      75%    max
    total_bill    244  19.785943  8.902412  3.07  13.3475  17.795  24.1275  50.81
    tip           244   2.998279  1.383638  1.00   2.0000   2.900   3.5625  10.00
    size          244   2.569672  0.951100  1.00   2.0000   2.000   3.0000   6.00
  • Find the mean tip broken down by gender and day

    [~]$ p.example_data -d tips | p.df 'df.groupby(by=["sex","day"]).tip.mean()' -o table index
    sex    day
    Female Fri   2.781111
           Sat   2.801786
           Sun   3.367222
           Thur  2.575625
    Male   Fri   2.693000
           Sat   3.083898
           Sun   3.220345
           Thur  2.980333

Join files on key fields

Pandashells can join files based on a set of key fields. This example uses only one field as a key, but like the pandas merge function on which it is based, multiple key fields can be used for the join.

  • Show poll resultes for the 2008 US presidential election

    [~]$ p.example_data -d election | p.df -o table | head  
         days state  obama  mccain                           poll
         -305    OH     43      50                      SurveyUSA
         -303    PA     38      46                      Rasmussen
         -298    OR     47      47                      SurveyUSA
         -298    WA     52      43                      SurveyUSA
         -294    AL     29      63                      SurveyUSA
         -294    NY     44      42                    Siena Coll.
         -294    VA     40      52                      SurveyUSA
         -290    NM     41      50                      SurveyUSA
         -290    NY     49      43                      SurveyUSA
  • Show population and electoral college numbers for states

    [~]$ p.example_data -d electoral_college | p.df -o table | head 
         state            name  electors  population
            AK          Alaska         3      710000
            AL         Alabama         9     4780000
            AR        Arkansas         6     2916000
            AZ         Arizona        11     6392000
            CA      California        55    37254000
            CO        Colorado         9     5029000
            CT     Connecticut         7     3574000
            DC   Dist. of Col.         3      602000
            DE        Delaware         3      898000
  • Join poll and electoral-college data (Note the use of bash process substitution to specify files to join.)

    [~]$ p.merge <(p.example_data -d election) <(p.example_data -d electoral_college) --how left --on state | p.df -o table | head 
         days state  obama  mccain                           poll            name  electors  population
         -252    AK     43      48                      SurveyUSA          Alaska         3      710000
         -213    AK     43      48                      Rasmussen          Alaska         3      710000
         -176    AK     41      50                      Rasmussen          Alaska         3      710000
         -143    AK     41      45                      Rasmussen          Alaska         3      710000
         -112    AK     40      45                      Rasmussen          Alaska         3      710000
          -99    AK     39      44                      Rasmussen          Alaska         3      710000
          -65    AK     35      54            Ivan Moore Research          Alaska         3      710000
          -58    AK     33      64                      Rasmussen          Alaska         3      710000
          -56    AK     39      55                            ARG          Alaska         3      710000

Visualization Tools

Pandashells provides a number of visualization tools to help you quickly explore your data. All visualizations are automatically configured to show an interactive plot using the configured backend (default is TkAgg, but can be configured with the p.config tool).

As of version 0.2.0 pandashells supports the WebAgg backend for matplotlib. This enables interactive plots to be shown in a browser. To run with this option, set configuration with

p.config --plot_backend WebAgg

Note that WebAgg requires that Tornado be installed. This installation is left up to the user.

The visualizations can also be saved to image files (e.g. .png) or rendered to html. The html generated can either be opened directly in the browser to show an interactive plot (using mpld3), or can be embedded in an existing html file. The examples below show Pandashells-created png images along with the command used to generate them.

  • Simple xy scatter plots

    [~]$ p.example_data -d tips | p.plot -x total_bill -y tip -s 'o' --title 'Tip Vs Bill' 

    Output Image

  • Faceted plots

    [~]$ p.example_data -d tips | p.facet_grid --row smoker --col sex --hue day --map pl.scatter --args total_bill tip --kwargs 'alpha=.2' 's=100' 

    Output Image

  • Histograms plots (Note the use of bash process substitution to paste two outputs together.)

    [~]$ paste <(p.rand -t normal -n 10000 | p.df --names normal) <(p.rand -t gamma -n 10000 | p.df --names gamma) | p.hist -i table -c normal gamma 

    Output Image

  • Empirical cumulative distribution plots

    [~]$ p.rand -t normal -n 500 | p.cdf -c value --names value 

    Output Image

Spectral Estimation

  • Plot a time series over which to compute a spectrum

    [~]$ p.example_data -d sealevel | p.plot -x year -y sealevel_mm 

    Output Image

  • Plot the spectrum

    [~]$ p.example_data -d sealevel | p.lomb_scargle -t year -y sealevel_mm --interp_exp 3 | p.plot -x period -y amp --xlim 0 1.5 --ylim 0 6.5 --xlabel 'Period years' --ylabel 'Amplitude (mm)' --title 'Global Sea Surface Height Spectrum' 

    Output Image

Linear Regression

Pandashells leverages the excellent Seaborn and Statsmodels libraries to handle linear regression.

  • Quick and dirty fit to a line

    [~]$ p.linspace 0 10 20 | p.df 'df["y_true"] = .2 * df.x' 'df["noise"] = np.random.randn(20)' 'df["y"] = df.y_true + df.noise' --names x | p.regplot -x x -y y 

    Output Image

  • Multi-variable linear regression

    [~]$p.example_data -d sealevel | p.df 'df["sin"]=np.sin(2*np.pi*df.year)' 'df["cos"]=np.cos(2*np.pi*df.year)' | p.regress -m 'sealevel_mm ~ year + sin + cos' 
                                OLS Regression Results
    Dep. Variable:            sealevel_mm   R-squared:                       0.961
    Model:                            OLS   Adj. R-squared:                  0.961
    Method:                 Least Squares   F-statistic:                     6442.
    Date:                Mon, 27 Jul 2015   Prob (F-statistic):               0.00
    Time:                        23:28:11   Log-Likelihood:                -2234.0
    No. Observations:                 780   AIC:                             4476.
    Df Residuals:                     776   BIC:                             4495.
    Df Model:                           3
    Covariance Type:            nonrobust
                     coef    std err          t      P>|t|      [95.0% Conf. Int.]
    Intercept  -6500.1722     47.829   -135.903      0.000     -6594.063 -6406.282
    year           3.2577      0.024    136.513      0.000         3.211     3.305
    sin           -4.6933      0.217    -21.650      0.000        -5.119    -4.268
    cos            1.4061      0.214      6.566      0.000         0.986     1.826
    Omnibus:                        5.332   Durbin-Watson:                   0.709
    Prob(Omnibus):                  0.070   Jarque-Bera (JB):                5.401
    Skew:                          -0.189   Prob(JB):                       0.0672
    Kurtosis:                       2.846   Cond. No.                     6.29e+05
    [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    [2] The condition number is large, 6.29e+05. This might indicate that there are
    strong multicollinearity or other numerical problems.

Further examples of each tool can be seen by calling it with the -h switch. You are encouraged to fully explore these examples. They highlight how Pandashells can be used to significantly improve your efficiency.

Simple Profiling Utility

In addition to command-line tools, Pandashells exposes a useful profiling tool that can be imported into your python code. The tools is just a simple context manager that sends timing information to stdout. The csv-like format of this output makes it easy to pipe through Pandashells pipelines. Here are a couple examples.

Profiling different parts of your code


import time
from pandashells import Timer
with Timer('entire script'):
    for nn in range(3):
        with Timer('loop {}'.format(nn + 1)):
            time.sleep(.1 * nn)
# Will generate the following output on stdout
#     col1: a string that is easily found with grep
#     col2: the time in seconds (or in hh:mm:ss if pretty=True)
#     col3: the value passed to the 'name' argument of Timer


__time__,2.6e-05,loop 1
__time__,0.105134,loop 2
__time__,0.204489,loop 3
__time__,0.310102,entire script

Profiling how code scales (measuring "big-O")


import time
from pandashells import Timer

# initialize a list to hold results
results = []

# run a piece of code with different values of the var you want to scale
for nn in range(3):
    # time each iteration
    with Timer('loop {}'.format(nn + 1), silent=True) as timer:
        time.sleep(.1 * nn)
    # add results
    results.append((nn, timer))

# print csv compatible text for further pandashells processing/plotting
print 'nn,seconds'
for nn, timer in results:
    print '{},{}'.format(nn,timer.seconds)

Projects by robdmc.

  • Pandashells Pandas at the bash command line
  • Consecution Pipeline abstraction for Python
  • Behold Helping debug large Python projects
  • Crontabs Simple scheduling library for Python scripts
  • Switchenv Manager for bash environments