Interactive Python Matplotlib with Pandas and NumPy

Example Code Snippets

Here are some example code snippets to get you started with Python Matplotlib:

Simple Bar Plot

import matplotlib.pyplot as plt

# Example Data (if using Pandas)
import pandas as pd
import numpy as np

data = pd.DataFrame({
    'species': ['Adelie', 'Chinstrap', 'Gentoo', 'Adelie', 'Chinstrap', 'Gentoo'],
    'flipper_length_mm': [181, 195, 210, 190, 200, 215]
})

# Grouping Data by Species and Getting Mean Flipper Length
species_avg = data.groupby('species')['flipper_length_mm'].mean()

# Plot
plt.bar(species_avg.index, species_avg.values)
plt.xlabel('Species')
plt.ylabel('Average Flipper Length (mm)')
plt.title('Penguin Flipper Length by Species')
plt.show()

Filtering

import matplotlib.pyplot as plt

# Example Data (if using Pandas)
import pandas as pd
import numpy as np

data = pd.DataFrame({
    'island': ['Biscoe','Dream','Biscoe','Biscoe','Biscoe'],
    'body_mass_g': [5200.0, 3000.0, 3175.0, 3175.0, 3175.0],
    'sex': ['female','male','female','female',np.nan]
})


print(f'Initial table:\n{data}\n' + '-'*30)

data = data.dropna()  # Drops rows where at least one value is NaN
data = data.drop_duplicates()  # Removes duplicate rows
print(f'After dropna+drop_duplicates:\n{data}\n' + '-'*30)

score = data['body_mass_g'] - data['body_mass_g'].mean()
print(f'Init Scores:\n{score}\n' + '-'*30)

score = np.abs(score)
print(f'After abs:\n{score}\n' + '-'*30)

data = data[score < np.std(data['body_mass_g'])]

print(f'filtered data:\n{data}\n' + '-'*30)


data_female = data[data['sex'] == 'female']
print(f'data_female:\n{data_female}\n' + '-'*30)

print(f'len(data_female)={len(data_female)}')

Filtering details

The filtering process in the above code snippet involves the following steps:

1. Drop rows with at least one NaN value using dropna().
2. Remove duplicate rows using drop_duplicates().
3. Calculate the score as the difference between the body mass and the mean body mass.
4. Take the absolute value of the score using np.abs().
5. Filter the data based on the condition that the score is less than the standard deviation of the body mass: data[score < np.std(data['body_mass_g'])].

`plt.hist` Example

import pandas as pd

csv = """
species,island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,sex
Adelie,Torgersen,39.1,18.7,181,3750,MALE
Adelie,Torgersen,34.4,18.4,184,3325,FEMALE
Adelie,Torgersen,46,21.5,194,4200,MALE
Adelie,Biscoe,37.8,18.3,174,3400,FEMALE
Chinstrap,Dream,50.5,18.4,200,3400,FEMALE
Chinstrap,Dream,49.5,19,200,3800,MALE
Chinstrap,Dream,46.4,17.8,191,3700,FEMALE
Chinstrap,Dream,42.5,16.7,187,3350,FEMALE
Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE
Gentoo,Biscoe,48.4,14.6,213,5850,MALE
Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE
Gentoo,Biscoe,46.2,14.9,221,5300,MALE
"""


# Convert string to file-like object
csv_io = io.StringIO(csv)

# Read the CSV
data = pd.read_csv(csv_io)

# Display first 5 rows
print(data.head(5))

import matplotlib.pyplot as plt

groupd = data.groupby('species')

for species, species_data in groupd:
    plt.hist(species_data['flipper_length_mm'], label=species, alpha=0.5)

plt.legend()
plt.show()

Scatter Plot Example

import pandas as pd

csv = """
species,island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,sex
Adelie,Torgersen,39.1,18.7,181,3750,MALE
Adelie,Torgersen,34.4,18.4,184,3325,FEMALE
Adelie,Torgersen,46,21.5,194,4200,MALE
Adelie,Biscoe,37.8,18.3,174,3400,FEMALE
Chinstrap,Dream,50.5,18.4,200,3400,FEMALE
Chinstrap,Dream,49.5,19,200,3800,MALE
Chinstrap,Dream,46.4,17.8,191,3700,FEMALE
Chinstrap,Dream,42.5,16.7,187,3350,FEMALE
Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE
Gentoo,Biscoe,48.4,14.6,213,5850,MALE
Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE
Gentoo,Biscoe,46.2,14.9,221,5300,MALE
"""


# Convert string to file-like object
csv_io = io.StringIO(csv)

# Read the CSV
data = pd.read_csv(csv_io)

# Display first 5 rows
print(data.head(5))

import matplotlib.pyplot as plt

groupd = data.groupby('species')

print(groupd)


plt.scatter(data['species'], data['flipper_length_mm'])

plt.legend()
plt.show()

Note that `plt.scatter(data['species'], data['flipper_length_mm'])` creates a scatter plot with the species on the x-axis and the flipper length on the y-axis.