A thin wrapper to make Seaborn charts from the command line.

chartmake.py

#!/usr/bin/env python3
#

# Here we parse the arguments and check their validity BEFORE importing the heavy libraries, just in case. Saves CPU.

# Arguments parsing
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-f", help="File name")
parser.add_argument("-c", help="Chart type. To view list of support charts, use --charts")
parser.add_argument("-x", help="X column to be passed with chart type")
parser.add_argument("-y", help="Y column to be passed with chart type")
parser.add_argument("--hue", help="Will color the chart based on this variable, if supported.")
parser.add_argument("--charts", help="List all available chart types - only pass this", action="store_true")
parser.add_argument("--color", help="Color scheme of the chart, if supported. Takes a hex code without leading #")
parser.add_argument("--theme", help="A seaborn theme")

args=parser.parse_args()

# So we only have to configure this once
def chartsShow():
    print("Current types supported:")
    print("    [type] <desc> (flags, [optional-flags])")
    print("    -")
    print("    [dist] Distribution plot (data, X)")
    print("    [bar] Bar plot (data, X, Y, [hue])")
    print("    [scatter] Scatter plot (data, X, Y, [hue])")
    print("    [hex] Hexagon joint plot (data, X, Y, [color])")    

# Flag preflight check
if args.charts:
    chartsShow()
    exit(0)

if not args.f:
    print("You need to include a filename")
    exit(1)

if not args.c:
    print("You must include a chart type")
    chartsShow()
    exit(1)

# Imports AFTER flag checks, to save CPU
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import datetime

# Set the theme if passed
if args.theme:
    sns.set_style(args.theme)
else:
    sns.set()

# Load CSV into Pandas
try:
    df = pd.read_csv(args.f)
except:
    print("Either your file doesn't exist or is not a real CSV. Please pass CSVs only.")
    exit(1)

# Arguments and Chart Building
if args.c == "dist":
    sns.distplot(df[args.x])
    figure_file_name = "dist-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
    plt.savefig(figure_file_name)
    print(f"Chart saved to ./{figure_file_name}")
    exit(0)


if args.c == "bar":
    if args.hue:
        sns.barplot(data=df, x=args.x, y=args.y, hue=args.hue)
        figure_file_name = "bar-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)
    else:
        sns.barplot(data=df, x=args.x, y=args.y)
        figure_file_name = "bar-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)


if args.c == "scatter":
    if args.hue:
        sns.scatterplot(data=df, x=args.x, y=args.y, hue=args.hue)
        figure_file_name = "scatter-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)
    else:
        sns.scatterplot(data=df, x=args.x, y=args.y)
        figure_file_name = "scatter-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)


if args.c == "hex":
    if args.color:
        sns.jointplot(data=df, x=args.x, y=args.y, color=f"#{args.color}", kind='hex')
        figure_file_name = "scatter-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)
    else:
        sns.jointplot(data=df, x=args.x, y=args.y, kind='hex')
        figure_file_name = "scatter-" + str(datetime.datetime.now()).replace(" ", "-") + ".png"
        plt.savefig(figure_file_name)
        print(f"Chart saved to ./{figure_file_name}")
        exit(0)

Run ./chartmake.py --help.

Requirements: pip3 install pandas seaborn numpy matplotlib