How to plot certain rows of a Pandas dataframe using Matplotlib?

To plot certain rows of a Pandas DataFrame, you can use iloc[] to slice specific rows and then apply the plot() method. This is useful when you want to visualize only a subset of your data.

Setting Up the Environment

First, let's import the necessary libraries and create a sample DataFrame ?

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Set figure size for better visualization
plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.autolayout"] = True

# Create a DataFrame with random data
np.random.seed(42)  # For reproducible results
df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde'))
print("Full DataFrame:")
print(df)

Full DataFrame:
          a         b         c         d         e
0  0.496714 -0.138264  0.647689  1.523030 -0.234153
1 -0.234137  1.579213  0.767435 -0.469474  0.542560
2  0.241962 -1.913280 -1.724918 -0.562288 -1.012831
3  0.314247 -0.908024 -1.412304  0.067528 -0.943725
4  0.896775 -0.601763  1.484537 -0.013497  1.761260
5 -0.845206 -1.478522 -0.635846 -1.469645  0.154947
6 -1.337070  0.844622 -0.743234  0.319039 -0.879236
7  0.195222  1.006757  0.458346 -1.207814 -1.424065
8  0.156349  0.230794  0.533601  0.967297  2.236161
9 -0.720589  0.298238 -1.203666  0.539249 -1.186896

Plotting Specific Rows

Now let's plot only the first 6 rows using iloc[] slicing ?

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.autolayout"] = True

# Create DataFrame
np.random.seed(42)
df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde'))

# Plot only first 6 rows
df.iloc[0:6].plot(y='e', marker='o', linewidth=2, markersize=8)
plt.title('Plot of Column "e" for First 6 Rows')
plt.xlabel('Row Index')
plt.ylabel('Values')
plt.grid(True, alpha=0.3)
plt.show()

print("Data being plotted (first 6 rows):")
print(df.iloc[0:6])

Data being plotted (first 6 rows):
          a         b         c         d         e
0  0.496714 -0.138264  0.647689  1.523030 -0.234153
1 -0.234137  1.579213  0.767435 -0.469474  0.542560
2  0.241962 -1.913280 -1.724918 -0.562288 -1.012831
3  0.314247 -0.908024 -1.412304  0.067528 -0.943725
4  0.896775 -0.601763  1.484537 -0.013497  1.761260
5 -0.845206 -1.478522 -0.635846 -1.469645  0.154947

Plotting Multiple Columns from Specific Rows

You can also plot multiple columns from the selected rows ?

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

plt.rcParams["figure.figsize"] = [10, 6]
np.random.seed(42)
df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde'))

# Plot multiple columns for rows 2 to 8
df.iloc[2:8][['c', 'd', 'e']].plot(kind='line', marker='o', linewidth=2)
plt.title('Multiple Columns Plot for Rows 2-7')
plt.xlabel('Row Index')
plt.ylabel('Values')
plt.legend(title='Columns')
plt.grid(True, alpha=0.3)
plt.show()

Different Row Selection Methods

Here are various ways to select and plot specific rows ?

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

np.random.seed(42)
df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde'))

fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Method 1: First n rows
df.iloc[:4].plot(y='a', ax=axes[0,0], title='First 4 rows', marker='o')

# Method 2: Last n rows  
df.iloc[-4:].plot(y='b', ax=axes[0,1], title='Last 4 rows', marker='s')

# Method 3: Specific row range
df.iloc[3:7].plot(y='c', ax=axes[1,0], title='Rows 3-6', marker='^')

# Method 4: Non-consecutive rows
df.iloc[[0, 2, 4, 6]].plot(y='d', ax=axes[1,1], title='Even-indexed rows', marker='D')

plt.tight_layout()
plt.show()

Key Points

• iloc[start:end] selects rows from start to end-1
• iloc[:n] selects the first n rows
• iloc[-n:] selects the last n rows
• iloc[[0,2,4]] selects specific non-consecutive rows
• Use plot(y='column_name') to plot a specific column

Conclusion

Use iloc[] with slicing to select specific rows from a DataFrame, then apply plot() to visualize the data. This approach is perfect for analyzing subsets of your data or creating focused visualizations from large datasets.