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Make Your Tabular Knowledge Stand Out through CLI With These Ideas and Methods | by Federico Trotta | Apr, 2023

Picture by Dorothe on Pixabay

A couple of days in the past I wished to assist my father resolve an issue. His want was to mixture, filter, and show some knowledge as quick as potential. Nicely…the reality is that he printed the information (one thing like 10 pages every time!!) and search the information by hand! I noticed his difficulties and determined to assist him instantly.

Nothing as tough for somebody who can analyze knowledge as I’m: the information was already in Excel format, so a Jupyter Pocket book and Pandas have been the proper selections.

The issue is that I don’t work for my father. Additionally, we go away in several cities and see one another solely each couple of weeks. So, I wanted to offer him a device he might use with the next traits:

  • Easy utilization.
  • Don’t make the PC explodes if he writes one thing improper.

That is why I believed to create a small program that may very well be managed through CLI. What I wished to create was straightforward: the person writes one thing through the command line. Then, this system exhibits the person all the information related as Pandas would do, however within the terminal.

So, on this article, I’ll present you the way we will show tabular knowledge through CLI (Command Line Interface. That’s: the terminal, in case you didn’t know). We’ll create a easy challenge to get you instantly hands-on Python and focus on the libraries I used.

So, initially, let’s create some tabular knowledge only for the sake of the train:

import pandas as pd

# Create knowledge

knowledge = {"fruit":["banana", "apple", "pear", "orange"],
"coloration":["yellow", "red", "green", "orange"],
"weight(kg)":[0.3, 0.1, 0.1, 0.2]


# Rework knowledge to knowledge body
df = pd.DataFrame(knowledge)

So, these are our knowledge:

The tabular knowledge we created. Picture by Creator.

We’ve created some tabular knowledge that comprise info on some fruits, notably: the title of the fruit, the colour, and the burden in kilograms.

Now, to make it “extra actual”, we will reserve it into an Excel file like so:

# Save knowledge body to xlsx file
This technique of saving recordsdata that Pandas offers us could be very helpful.
For instance, we will use it to transform CSV recordsdata into XLSX; We did it
in this text right here.

Now, we’ll create a easy filter that doesn’t want Python if we will use Excel a bit bit. The issue I confronted was extra sophisticated, however right here we’re creating it merely on function: our scope is to not present that this methodology is healthier than one other. Right here we’re exhibiting how we will show tabular knowledge through CLI, and a easy instance will do the job.

So, let’s say that is our drawback: we wish the person to write down the title of a fruit and our program returns all of the options of the fruit chosen. We additionally need the filter to be by some means “clever” in order that if the person writes “pea” it should show the options associated to “pear”.

To take action, in Pandas we will use the tactic str.incorporates(). Let’s attempt it in our Jupyter Pocket book:

import pandas as pd

# Import knowledge
df = pd.read_excel("fruit.xlsx")

# Filter for pear
data_frame = df[df["fruit"].str.incorporates("pea")]

# Present filtered knowledge

And we get:

The filtered knowledge. Picture by Creator.

Learn it fastidiously: we wrote “pea” as a typo on function to get certain Pandas returns the information anyway. And it does, as anticipated.

So, now we’ve to face one other drawback: the intervention of the person through CLI. So far as I do know, we will use two totally different strategies in these instances: we will use the enter built-in perform or we will use the library argparse.

In case you missed it, I’ve written an article on how we will use argparse in Knowledge Science. Test it out right here:

Now, on this case, I made a decision to make use of the enter built-in perform as a result of I consider it’s simpler to make use of, and in easy instances like these is an excellent alternative. The truth is, that is the proper alternative if we simply must cross a string as an argument through CLI (you’ll be able to learn the documentation right here).

We are able to use the enter perform like so:

# Consumer enter
fruit = enter("filter the information for the sort of fruit: ")

Now, let’s see how this works and the way it returns the information. That is the code we will use:

import pandas as pd

# Consumer enter
fruit = enter("filter the information for the sort of fruit: ")

# Import knowledge
df = pd.read_excel("fruit.xlsx")

# Filter for person enter
data_frame = df[df["fruit"].str.incorporates(fruit)]

# Print outcomes

have a look at the distinction of how we have pasted the arguments within the methodology
str.incorporates(). Aboved we have handed "pea" with quotes as a result of we have been
looking straight for a string.
On this case, insetead, we've handed "fruit" with out quotes as a result of
we've used "fruit" as a variable to invoke the enter() perform so it
needs to be handed as is (with no quotes).

Now, let’s reserve it as fruit.py, transfer it to the folder the place fruit.xlsx is positioned and let’s run it through the terminal:

Our code through CLI. GIF by Creator.

Nicely, as we will see, every thing works positive. However only one factor: can we enhance the visualization? What if we’d prefer to show higher the information as if we have been on Pandas?

Nicely, the answer I discovered was to make use of the library tabulate (right here’s the documentation).

So, let’s add tabulate to our code and see what occurs:

import pandas as pd
from tabulate import tabulate

# Consumer enter
fruit = enter("filter the information for the sort of fruit: ")

# Import knowledge
df = pd.read_excel("fruit.xlsx")

# Filter for person enter
data_frame = df[df["fruit"].str.incorporates(fruit)]

# Print outcomes
print(tabulate(data_frame, headers='keys', tablefmt='psql'))

And we get:

Our code through CLI. Picture by Creator.

As we will see, the information are displayed in a “tabular method”, which is cleaner. Additionally, as we will see, the code appropriately manages typos if we seek for “pea” as we did in Jupyter earlier than.

I hope this helps you if it’s essential to show tabular knowledge through CLI. You probably have every other options, please, let me know within the feedback: I’m at all times open to bettering and studying one thing new.



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