Python For Data Science

The df.plot() function provides a simple way to produce a variety of plot types, including line plots, bar plots, histograms, scatter plots, and more. Here's how it works and some examples to demonstrate its capabilities.

EnrollThe `df.plot()`

function in pandas is a flexible and easy way to create plots from data in DataFrame objects. It is built on top of Matplotlib, a comprehensive library for creating static, animated, and interactive visualizations in Python. The `df.plot()`

function provides a simple way to produce a variety of plot types, including line plots, bar plots, histograms, scatter plots, and more. Here's how it works and some examples to demonstrate its capabilities.

The basic syntax of the `df.plot()`

function is:

`DataFrame.plot(x=None, y=None, kind='line', ax=None, figsize=None, title=None, grid=None, legend=True, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds)`

**x, y**: Labels of the columns in the DataFrame to be used as x and y coordinates.**kind**: The type of plot to produce:`'line'`

(default): Line plot`'bar'`

or`'barh'`

: Bar plot (horizontal or vertical)`'hist'`

: Histogram`'box'`

: Boxplot`'kde'`

or`'density'`

: Kernel Density Estimation plot`'area'`

: Area plot`'pie'`

: Pie plot`'scatter'`

: Scatter plot (requires`x`

and`y`

)`'hexbin'`

: Hexbin plot (requires`x`

and`y`

)

**ax**: An instance of Matplotlib's`axes`

object. Allows plotting on a specific subplot.**figsize**: A tuple (width, height) in inches for the figure size.**title**: Title of the plot.**grid**: Boolean value to display or hide grid lines.**legend**: Boolean value to display or hide the legend.- Other parameters provide more customization like log scale, axis limits, rotations, font size, etc.

Examples: Let's create some example plots using a sample DataFrame.Each of these examples demonstrates a different type of plot you can create using the `df.plot()`

function. By adjusting the parameters and the data you pass to the function, you can generate a wide range of visualizations to explore and present your data.

```
import pandas as pd
import numpy as np
# Sample DataFrame
df = pd.DataFrame({
'A': np.random.randn(50).cumsum(),
'B': np.random.randn(50).cumsum(),
'C': np.random.randn(50).cumsum()
})
# Line Plot
df.plot(title='Line Plot')
# Bar Plot
df.plot(kind='bar', title='Bar Plot')
# Histogram
df.plot(kind='hist', alpha=0.7, title='Histogram')
# Scatter Plot
df.plot(kind='scatter', x='A', y='B', title='Scatter Plot', color='red')
# Area Plot
df.plot(kind='area', stacked=False, title='Area Plot')
```

Lesson Assignment

Challenge yourself with our lab assignment and put your skills to test.

```
# Python Program to find the area of triangle
a = 5
b = 6
c = 7
# Uncomment below to take inputs from the user
# a = float(input('Enter first side: '))
# b = float(input('Enter second side: '))
# c = float(input('Enter third side: '))
# calculate the semi-perimeter
s = (a + b + c) / 2
# calculate the area
area = (s*(s-a)*(s-b)*(s-c)) ** 0.5
print('The area of the triangle is %0.2f' %area)
```

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