Pandas Plotting

Pandas Plotting – Complete Guide (Beginner → Advanced)

Pandas has built-in plotting powered by Matplotlib, so you can visualize data with one line of code.

1. Setup (Required)

Pandas uses matplotlib behind the scenes

2. Create Sample Data

3. Basic Line Plot (Beginner)

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220],
    "profit": [20, 30, 25, 40, 55]
}

# Create DataFrame
df = pd.DataFrame(data)

# Line plot
df.plot(x="year", y="sales")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220],

"profit": [20, 30, 25, 40, 55]

}

# Create DataFrame

df = pd.DataFrame(data)

# Line plot

df.plot(x="year", y="sales")

# Show plot

plt.show()

Automatically plots all numeric columns
Index used as X-axis

Plot Single Column

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220],
    "profit": [20, 30, 25, 40, 55]
}

# Create DataFrame
df = pd.DataFrame(data)

# Plot single column (sales)
df["sales"].plot()

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220],

"profit": [20, 30, 25, 40, 55]

}

# Create DataFrame

df = pd.DataFrame(data)

# Plot single column (sales)

df["sales"].plot()

# Show plot

plt.show()

4. Line Plot with Custom X-Axis

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220],
    "profit": [20, 30, 25, 40, 55]
}

# Create DataFrame
df = pd.DataFrame(data)

# Line plot with custom X-axis
df.plot(x="year", y="sales")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220],

"profit": [20, 30, 25, 40, 55]

}

# Create DataFrame

df = pd.DataFrame(data)

# Line plot with custom X-axis

df.plot(x="year", y="sales")

# Show plot

plt.show()

5. Multiple Line Plot

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220],
    "profit": [20, 30, 25, 40, 55]
}

# Create DataFrame
df = pd.DataFrame(data)

# Multiple line plot
df.plot(x="year", y=["sales", "profit"])

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220],

"profit": [20, 30, 25, 40, 55]

}

# Create DataFrame

df = pd.DataFrame(data)

# Multiple line plot

df.plot(x="year", y=["sales", "profit"])

# Show plot

plt.show()

Great for comparisons

6. Bar Chart

Vertical Bar

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220]
}

# Create DataFrame
df = pd.DataFrame(data)

# Vertical bar plot
df.plot(x="year", y="sales", kind="bar")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220]

}

# Create DataFrame

df = pd.DataFrame(data)

# Vertical bar plot

df.plot(x="year", y="sales", kind="bar")

# Show plot

plt.show()

Horizontal Bar

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220]
}

# Create DataFrame
df = pd.DataFrame(data)

# Horizontal bar plot
df.plot(x="year", y="sales", kind="barh")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220]

}

# Create DataFrame

df = pd.DataFrame(data)

# Horizontal bar plot

df.plot(x="year", y="sales", kind="barh")

# Show plot

plt.show()

7. Histogram (Data Distribution)

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "sales": [100, 150, 130, 180, 220, 160, 140, 200, 210, 170]
}

# Create DataFrame
df = pd.DataFrame(data)

# Histogram plot
df["sales"].plot(kind="hist", bins=5)

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"sales": [100, 150, 130, 180, 220, 160, 140, 200, 210, 170]

}

# Create DataFrame

df = pd.DataFrame(data)

# Histogram plot

df["sales"].plot(kind="hist", bins=5)

# Show plot

plt.show()

Shows frequency distribution
Interview favorite

8. Box Plot (Outliers Detection)

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "sales": [100, 150, 130, 180, 220, 160, 140, 200, 210, 170],
    "profit": [20, 30, 25, 40, 55, 35, 28, 45, 50, 38]
}

# Create DataFrame
df = pd.DataFrame(data)

# Box plot
df.plot(kind="box")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"sales": [100, 150, 130, 180, 220, 160, 140, 200, 210, 170],

"profit": [20, 30, 25, 40, 55, 35, 28, 45, 50, 38]

}

# Create DataFrame

df = pd.DataFrame(data)

# Box plot

df.plot(kind="box")

# Show plot

plt.show()

Detects:

Outliers
Median
Quartiles

9. Area Plot

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220],
    "profit": [20, 30, 25, 40, 55]
}

# Create DataFrame
df = pd.DataFrame(data)

# Area plot
df.plot(x="year", y=["sales", "profit"], kind="area", alpha=0.6)

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220],

"profit": [20, 30, 25, 40, 55]

}

# Create DataFrame

df = pd.DataFrame(data)

# Area plot

df.plot(x="year", y=["sales", "profit"], kind="area", alpha=0.6)

# Show plot

plt.show()

What This Area Plot Shows

X-axis → year
Y-axis → sales & profit
Filled areas show magnitude over time
alpha=0.6 → transparency so areas don’t hide each other

Optional: Improve Readability

10. Scatter Plot (Relationship)

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "sales": [100, 150, 130, 180, 220, 160, 140, 200],
    "profit": [20, 30, 25, 40, 55, 35, 28, 45]
}

# Create DataFrame
df = pd.DataFrame(data)

# Scatter plot
df.plot(x="sales", y="profit", kind="scatter")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"sales": [100, 150, 130, 180, 220, 160, 140, 200],

"profit": [20, 30, 25, 40, 55, 35, 28, 45]

}

# Create DataFrame

df = pd.DataFrame(data)

# Scatter plot

df.plot(x="sales", y="profit", kind="scatter")

# Show plot

plt.show()

What This Scatter Plot Shows

X-axis → sales
Y-axis → profit
Each dot = one data point
Helps identify relationship / correlation

Optional: Improve Clarity

11. Pie Chart

import pandas as pd
import matplotlib.pyplot as plt

# Create data
data = {
    "year": [2019, 2020, 2021, 2022, 2023],
    "sales": [100, 150, 130, 180, 220]
}

# Create DataFrame
df = pd.DataFrame(data)

# Pie chart
df.set_index("year")["sales"].plot(
    kind="pie",
    autopct="%1.1f%%",
    startangle=90
)

# Remove y-axis label
plt.ylabel("")

# Show plot
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Create data

data = {

"year": [2019, 2020, 2021, 2022, 2023],

"sales": [100, 150, 130, 180, 220]

}

# Create DataFrame

df = pd.DataFrame(data)

# Pie chart

df.set_index("year")["sales"].plot(

kind="pie",

autopct="%1.1f%%",

startangle=90

)

# Remove y-axis label

plt.ylabel("")

# Show plot

plt.show()

What This Pie Chart Shows

Each slice represents yearly sales share
autopct="%1.1f%%" → shows percentage
startangle=90 → better visual orientation

Optional: Improve Look

12. Plot Styling (Intermediate)

13. Figure Size Control

14. Plot on Specific Axes (Advanced)

Overlay plots

15. Subplots (Advanced)

16. Save Plot to File

Used in reports & automation

17. Time Series Plot (Very Important)

Pandas automatically handles dates

18. Common Errors

1. Plot not showing
Add plt.show()

2. Too many columns plotted
Select columns explicitly

3. Overlapping labels
Increase figsize

19. Pandas vs Matplotlib

Feature	Pandas Plot	Matplotlib
Ease	Very easy	Moderate
Control	Limited	Full control
Best for	Quick plots	Advanced visuals

Rule

Quick EDA → Pandas
Publication plots → Matplotlib / Seaborn

20. Interview Questions (Must Know)

Q1: Which library Pandas uses for plotting?
Matplotlib

Q2: Default plot type in Pandas?
Line plot

Q3: How to plot histogram?
kind="hist"

Q4: How to plot multiple columns?
Pass list in y

Pandas Plotting

Pandas Plotting – Complete Guide (Beginner → Advanced)

1. Setup (Required)

2. Create Sample Data

3. Basic Line Plot (Beginner)

Plot Single Column

4. Line Plot with Custom X-Axis

5. Multiple Line Plot

6. Bar Chart

Vertical Bar

Horizontal Bar

7. Histogram (Data Distribution)

8. Box Plot (Outliers Detection)

9. Area Plot

What This Area Plot Shows

Optional: Improve Readability

10. Scatter Plot (Relationship)

What This Scatter Plot Shows

Optional: Improve Clarity

11. Pie Chart

What This Pie Chart Shows

Optional: Improve Look

12. Plot Styling (Intermediate)

13. Figure Size Control

14. Plot on Specific Axes (Advanced)

15. Subplots (Advanced)

16. Save Plot to File

17. Time Series Plot (Very Important)

18. Common Errors

19. Pandas vs Matplotlib

20. Interview Questions (Must Know)

Plot Type Cheat Sheet

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Pandas Plotting

Pandas Plotting – Complete Guide (Beginner → Advanced)

1. Setup (Required)

2. Create Sample Data

3. Basic Line Plot (Beginner)

Plot Single Column

4. Line Plot with Custom X-Axis

5. Multiple Line Plot

6. Bar Chart

Vertical Bar

Horizontal Bar

7. Histogram (Data Distribution)

8. Box Plot (Outliers Detection)

9. Area Plot

What This Area Plot Shows

Optional: Improve Readability

10. Scatter Plot (Relationship)

What This Scatter Plot Shows

Optional: Improve Clarity

11. Pie Chart

What This Pie Chart Shows

Optional: Improve Look

12. Plot Styling (Intermediate)

13. Figure Size Control

14. Plot on Specific Axes (Advanced)

15. Subplots (Advanced)

16. Save Plot to File

17. Time Series Plot (Very Important)

18. Common Errors

19. Pandas vs Matplotlib

20. Interview Questions (Must Know)

Plot Type Cheat Sheet

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