chat1

import polars as pl
import matplotlib.pyplot as plt
import math

def plot_dataframes_with_titles(dataframes_with_titles):
    """
    Plots scatter plots for a list of (title, DataFrame) tuples.
    
    Args:
    dataframes_with_titles (List[Tuple[str, pl.DataFrame]]): List of tuples where each tuple contains a title and a polars DataFrame.
    """
    num_plots = len(dataframes_with_titles)
    cols = math.ceil(math.sqrt(num_plots))
    rows = math.ceil(num_plots / cols)
    
    plt.figure(figsize=(5 * cols, 5 * rows))
    
    for i, (title, df) in enumerate(dataframes_with_titles):
        plt.subplot(rows, cols, i + 1)
        plt.scatter(df[:, 0], df[:, 1])
        plt.title(title)
        plt.xlabel(df.columns[0])
        plt.ylabel(df.columns[1])
    
    plt.tight_layout()
    plt.show()

# SampleExample DataFrameusage
dfdf1 = pl.DataFrame({
    "stuff"x": [None, None, 1, 1,2, 1,3, None,4, None,5],
 1,   1,"y": None,[10, 1,20, None,30, 1,40, 1, 1, 1]50]
})

# Create a forward-looking column to compare with the current 'stuff' column
dfdf2 = df.with_column(pl.DataFrame({
    pl.col("stuff").shift(-1).alias(x": [1, 2, 3, 4, 5],
    "next_stuff")y": [15, 25, 35, 45, 55]
})

# Identify rows where a change from 1 to None or None to 1 will occur
dfdf3 = df.with_column(pl.DataFrame({
    "x": [1, 2, 3, 4, 5],
    "y": [5, 15, 25, 35, 45]
})

dataframes_with_titles = [
    (pl.col("stuff")Scatter !=Plot pl.col(1", df1),
    ("next_stuff")).alias(Scatter Plot 2", df2),
    ("will_change")Scatter Plot 3", df3)
)]

# Count the number of changes from 1 to None, indicating the end of a group of 1s
count_groups = df.filter((pl.col("stuff") == 1) & (pl.col("next_stuff").is_null())).count()

print("Number of contiguous groups of 1s:", count_groups)plot_dataframes_with_titles(dataframes_with_titles)