Plotivy vs Julius AI: Choosing the Right AI Analyst

Sample code / prompt

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd

# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50

Sample code / prompt

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

# Generate gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20

Sample code / prompt

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

# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
    [1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
    [0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],

Sample code / prompt

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

# Generate bacterial growth data with replicates
np.random.seed(42)
time_points = np.array([0, 4, 8, 12, 18, 24])
mean_values = np.array([10, 25, 80, 250, 600, 800])

# Generate 5 replicates per time point with noise

Sample code / prompt

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway

# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)

Sample code / prompt

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm

# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80)  # Clip to realistic range

fig, ax = plt.subplots(figsize=(12, 7))