pip install pandas lightningchart

data = pd.read_csv(‘weatherHistory.csv’)
data = pd.read_csv(‘ContiguousU.S.ExtremesInMaximumTemperature.csv’)

import pandas as pd
import lightningchart as lc

# Set license key
lc.set_license('my_license_key')

# Load data
data = pd.read_csv('ContiguousU.S.ExtremesInMaximumTemperature.csv')

# Prepare data for the combined Bar Chart
categories = data['Date'].astype(str).tolist()
data_combined = [
    {'subCategory': 'Much Above Normal', 'values': data['Much Above Normal'].tolist()},
    {'subCategory': 'Much Below Normal', 'values': data['Much Below Normal'].tolist()}
]

# Create the combined Bar Chart
bar_chart = lc.BarChart(
    vertical=True,
    theme=lc.Themes.White,
    title='Contiguous U.S. Extremes in Maximum Temperature'
)

# Set data for the combined Bar Chart
bar_chart.set_data_grouped(categories, data_combined)
bar_chart.set_bars_margin(0.1)
bar_chart.open(method='browser')

import pandas as pd
from lightningchart import Themes, Chart3D

# Set license key
my_license_key = 'my_license_key'

# Load data
data = pd.read_csv('weatherHistory.csv')

# Calculate averages by weather type
avg_weather_data = data.groupby('Summary')[['Temperature (C)', 'Apparent Temperature (C)', 'Humidity', 'Wind Speed (km/h)']].mean().reset_index()

# Prepare data for 3D chart
categories = avg_weather_data['Summary'].tolist()
temperatures = avg_weather_data['Temperature (C)'].tolist()
apparent_temp_values = avg_weather_data['Apparent Temperature (C)'].tolist()
humidities = avg_weather_data['Humidity'].tolist()
wind_speeds = avg_weather_data['Wind Speed (km/h)'].tolist()

# Create 3D chart
chart_3d = Chart3D(
    theme=Themes.White,
    title='Average Temperature, Apparent Temperature, Humidity, and Wind Speed by Weather Type',
    license=my_license_key
)

# Add box series for temperature, apparent temperature, humidity, and wind speed
box_series_temp = chart_3d.add_box_series()
box_series_temp.set_name('Temperature')

data_boxes_temp = [
    {
        'xCenter': i * 4 + 1,
        'yCenter': temperatures[i] / 2,
        'zCenter': 0,
        'xSize': 4,
        'ySize': temperatures[i],
        'zSize': 0.2
    }
    for i in range(len(categories))
]

box_series_temp.add(data_boxes_temp)

box_series_temp_app = chart_3d.add_box_series()
box_series_temp_app.set_name('Apparent Temperature')

data_boxes_app_temp = [
    {
        'xCenter': i * 4 + 1,
        'yCenter': apparent_temp_values[i] / 2,
        'zCenter': 1,
        'xSize': 4,
        'ySize': apparent_temp_values[i],
        'zSize': 0.2
    }
    for i in range(len(categories))
]

box_series_temp_app.add(data_boxes_app_temp)

box_series_humidity = chart_3d.add_box_series()
box_series_humidity.set_name('Humidity')

data_boxes_humidity = [
    {
        'xCenter': i * 4 + 2,
        'yCenter': humidities[i] / 2,
        'zCenter': 2,
        'xSize': 4,
        'ySize': humidities[i],
        'zSize': 0.2
    }
    for i in range(len(categories))
]

box_series_humidity.add(data_boxes_humidity)

box_series_wind_speed = chart_3d.add_box_series()
box_series_wind_speed.set_name('Wind Speed')

data_boxes_wind_speed = [
    {
        'xCenter': i * 4 + 3,
        'yCenter': wind_speeds[i] / 2,
        'zCenter': 3,
        'xSize': 4,
        'ySize': wind_speeds[i],
        'zSize': 0.2
    }
    for i in range(len(categories))
]

box_series_wind_speed.add(data_boxes_wind_speed)

chart_3d.open(method='browser')

import pandas as pd
import lightningchart as lc
import numpy as np

# Set license key
lc.set_license('my_license_key')

# Load data
file_path = 'weatherHistory.csv'
data = pd.read_csv(file_path)

# Convert 'Formatted Date' to datetime
data['Formatted Date'] = pd.to_datetime(data['Formatted Date'], utc=True)

# Group data by weather summary and temperature
grouped_data = data.groupby('Summary')['Temperature (C)'].apply(list)

# Convert grouped data to list of lists for box plot
box_plot_data = grouped_data.tolist()

# Create XY chart
chart = lc.ChartXY(
    title='Temperature Distribution by Weather Summary',
    theme=lc.Themes.White
)

# Function to add box plot to chart
def add_box_plot_to_chart(chart, column_data, column_name, column_index):
    q1 = np.percentile(column_data, 25)
    q3 = np.percentile(column_data, 75)
    median = np.median(column_data)
    min_val = np.min(column_data)
    max_val = np.max(column_data)
    
    series = chart.add_box_series()
    series.add(
        start=column_index - 0.4,
        end=column_index + 0.4,
        median=float(median),
        lower_quartile=float(q1),
        upper_quartile=float(q3),
        lower_extreme=float(min_val),
        upper_extreme=float(max_val)
    )
    series.set_name(column_name)

# Add each weather summary as a separate box plot
for i, (label, values) in enumerate(zip(grouped_data.index, box_plot_data)):
    add_box_plot_to_chart(chart, values, label, i)

# Customize x-axis labels
x_axis = chart.get_default_x_axis()
x_axis.set_title('Weather Summary')
x_axis.set_scroll_strategy('fitting')

# Customize y-axis title
y_axis = chart.get_default_y_axis()
y_axis.set_title('Temperature (C)')

chart.open(method='browser')

import pandas as pd
import lightningchart as lc

# Load data
file_path = 'weatherHistory.csv'
data = pd.read_csv(file_path)

# Set license key
lc.set_license('my_license_key')

# Extract temperature and humidity data
temperature = data['Temperature (C)'].tolist()
humidity = data['Humidity'].tolist()

# Create Scatter Chart
chart = lc.ScatterChart(
    theme=lc.Themes.White,
    title='Temperature vs Humidity',
    xlabel='Temperature (C)',
    ylabel='Humidity',
    point_shape='circle',
    individual_colors=False
)

# Add data to Scatter Chart
series = chart.series.append_samples(
    x_values=temperature,
    y_values=humidity
)

# Set palette colors based on temperature values
series.set_palette_colors(
    steps=[
        {'value':

 -10, 'color': '#0000FF'},
        {'value': 0, 'color': '#00FFFF'},
        {'value': 10, 'color': '#00FF00'},
        {'value': 20, 'color': '#FFFF00'},
        {'value': 30, 'color': '#FF0000'}
    ]
)

chart.open(method='browser')

import pandas as pd
import lightningchart as lc

# Set license key
lc.set_license('my_license_key')

# Load data
file_path = 'weatherHistory.csv'
data = pd.read_csv(file_path)

# Group data by weather summary
grouped_data = data['Summary'].value_counts()

# Create Pie Chart
chart = lc.PieChart(
    theme=lc.Themes.Dark,
    title='Distribution of Weather Types'
)

# Add data to Pie Chart
for category, value in grouped_data.items():
    chart.add_slice(value, category)

chart.open(method='browser')