Plotting a Line chart

A line chart (or line plot) is a graph that shows the frequency of data along a number line. It is used to display a continuous dataset — meaning data that flows without gaps, like measurements over time. The main purpose of a line chart is to visualise growth or decline in data over a time interval. You have already seen simple line charts in Programs 4-1 and 4-2. Now we learn to plot a line chart for data stored in a DataFrame.

The textbook uses a real-world example: Smile NGO participated in a three-week cultural mela. They stored day-wise sales (in rupees) for each week in a CSV file named "MelaSales.csv". The data has three columns — Week 1, Week 2, and Week 3 — with seven rows (one for each day of the week). The sales figures are:

The goal is to depict the sales for all three weeks using a single line chart with these specifications: chart title "Mela Sales Report", x-axis label "Days", y-axis label "Sales in Rs", and line colours red for week 1, blue for week 2, and brown for week 3.

Here is the code that achieves this:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("MelaSales.csv")
df.plot(kind='line', color=['red','blue','brown'])
plt.title('Mela Sales Report')
plt.xlabel('Days')
plt.ylabel('Sales in Rs')
plt.show()

Notice that df.plot(kind='line', ...) is called directly on the DataFrame. The color parameter accepts a list of colours — one for each column in the DataFrame. The order of colours matches the order of columns: first colour for the first column (Week 1), second for Week 2, third for Week 3.

When you run this code, Matplotlib automatically places the row index (0, 1, 2, ..., 6) on the x-axis. This is because a line plot expects numeric values on the x-axis, and by default it uses the DataFrame's index. The legend is displayed by default, associating each colour with its corresponding week column. The output is Figure 4.5 in the textbook.

Customising the Line Plot

You can replace the default numeric x-axis ticks with custom labels using plt.xticks(ticks, label). Here, ticks is a list of positions (locations) on the x-axis where you want ticks placed, and label is a list of items to display at those positions.

The textbook extends the same mela sales example with additional customisations: marker style, marker size, line style, and line width. The specifications are:

• Marker = "*" (asterisk)
• Marker size = 10
• Linestyle = "--" (dashed)
• Linewidth = 3

The code for this customised plot is:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("MelaSales.csv")
df.plot(kind='line', color=['red','blue','brown'],
        marker="*", markersize=10, linewidth=3, linestyle="--")
plt.title('Mela Sales Report')
plt.xlabel('Days')
plt.ylabel('Sales in Rs')

ticks = df.index.tolist()
plt.xticks(ticks, df.Day)
plt.show()

A few important details:

• The marker, markersize, linewidth, and linestyle parameters are passed inside df.plot(). They apply to all lines in the plot.
• df.index.tolist() converts the index (0, 1, 2, ..., 6) into a Python list. This list is used as the ticks argument. …

The real book's own 'Customising Line Plot' content is Program 4-5 above — the same MelaSales.csv chart styled with marker='*', markersize=10, linewidth=3, linestyle='--', and plt.xticks(ticks, df.Day). (No separate plt.plot(fmt_string, ...) shorthand or unrelate …