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update(candlestick-basic): seaborn — comprehensive quality review
Comprehensive quality review of seaborn implementation for candlestick-basic.
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‎plots/candlestick-basic/implementations/seaborn.py‎

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""" pyplots.ai
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candlestick-basic: Basic Candlestick Chart
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 90/100 | Created: 2025-12-23
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Library: seaborn 0.13.2 | Python 3.14.3
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Quality: /100 | Updated: 2026-02-24
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from matplotlib.patches import Patch
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# Set seaborn style
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sns.set_theme(style="whitegrid")
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from matplotlib.collections import PatchCollection
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from matplotlib.patches import Patch, Rectangle
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# Seaborn theme and context
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sns.set_theme(
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style="whitegrid",
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rc={
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"axes.facecolor": "#f8f9fa",
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"figure.facecolor": "white",
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"grid.color": "#dee2e6",
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"text.color": "#212529",
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"axes.labelcolor": "#495057",
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"xtick.color": "#495057",
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"ytick.color": "#495057",
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},
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)
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sns.set_context("talk", font_scale=1.2)
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up_fill, up_edge = "#2978b5", "#1b5a8a"
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down_fill, down_edge = "#e07a3a", "#b35a1f"
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# Data - 30 days of simulated stock OHLC data
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# Data - 30 trading days with rally then selloff pattern
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np.random.seed(42)
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n_days = 30
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dates = pd.date_range("2024-01-02", periods=n_days, freq="B") # Business days
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# Generate realistic OHLC data
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price = 150.0
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dates = pd.date_range("2024-01-02", periods=n_days, freq="B")
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price = 145.0
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drift = np.concatenate(
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[
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np.linspace(0.4, 0.8, 12), # Uptrend phase
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np.linspace(-0.1, -0.6, 18), # Reversal and selloff
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]
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)
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opens, highs, lows, closes = [], [], [], []
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for _ in range(n_days):
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# Daily volatility
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change = np.random.randn() * 3
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daily_range = abs(np.random.randn()) * 2 + 1
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for i in range(n_days):
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change = drift[i] + np.random.randn() * 2.5
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volatility = abs(np.random.randn()) * 1.5 + 0.8
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open_price = price
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close_price = price + change
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# High and low include the open/close range plus some extra
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high_price = max(open_price, close_price) + abs(np.random.randn()) * daily_range
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low_price = min(open_price, close_price) - abs(np.random.randn()) * daily_range
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high_price = max(open_price, close_price) + abs(np.random.randn()) * volatility
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low_price = min(open_price, close_price) - abs(np.random.randn()) * volatility
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opens.append(open_price)
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highs.append(high_price)
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lows.append(low_price)
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closes.append(close_price)
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price = close_price
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df = pd.DataFrame({"date": dates, "open": opens, "high": highs, "low": lows, "close": closes})
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# Determine bullish (up) vs bearish (down) days - use colorblind-safe colors
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df["bullish"] = df["close"] >= df["open"]
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# Colorblind-safe: blue for up, orange for down
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color_up = "#1f77b4" # Blue for bullish
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color_down = "#ff7f0e" # Orange for bearish
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df["color"] = df["bullish"].map({True: color_up, False: color_down})
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df["x"] = range(len(df))
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df["x"] = range(n_days)
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# Create plot
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# Plot
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fig, ax = plt.subplots(figsize=(16, 9))
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# Draw candlesticks manually with seaborn styling applied
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body_width = 0.6
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# Wicks
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wick_colors = [up_edge if b else down_edge for b in df["bullish"]]
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ax.vlines(df["x"], df["low"], df["high"], colors=wick_colors, linewidth=1.5)
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# Candle bodies via PatchCollection
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body_width = 0.6
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rects, fcolors, ecolors = [], [], []
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for _, row in df.iterrows():
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x = row["x"]
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color = row["color"]
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# Draw wick (high-low line) using seaborn's lineplot via dataframe
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wick_data = pd.DataFrame({"x": [x, x], "price": [row["low"], row["high"]]})
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sns.lineplot(data=wick_data, x="x", y="price", ax=ax, color=color, linewidth=2, legend=False)
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# Draw candle body using matplotlib Rectangle for precise control
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body_bottom = min(row["open"], row["close"])
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body_height = abs(row["close"] - row["open"])
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# Ensure minimum body height for doji candles
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if body_height < 0.2:
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body_height = 0.2
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body_bottom = (row["open"] + row["close"]) / 2 - 0.1
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rect = plt.Rectangle(
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(x - body_width / 2, body_bottom), body_width, body_height, facecolor=color, edgecolor=color, linewidth=1
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)
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ax.add_patch(rect)
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# Set x-axis to show dates
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tick_positions = range(0, n_days, 5) # Show every 5th date
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tick_labels = [df["date"].iloc[i].strftime("%b %d") for i in tick_positions]
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ax.set_xticks(tick_positions)
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ax.set_xticklabels(tick_labels, rotation=0)
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# Style the plot
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bottom = min(row["open"], row["close"])
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height = max(abs(row["close"] - row["open"]), 0.15)
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if abs(row["close"] - row["open"]) < 0.15:
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bottom = (row["open"] + row["close"]) / 2 - 0.075
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rects.append(Rectangle((row["x"] - body_width / 2, bottom), body_width, height))
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fcolors.append(up_fill if row["bullish"] else down_fill)
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ecolors.append(up_edge if row["bullish"] else down_edge)
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bodies = PatchCollection(rects, facecolors=fcolors, edgecolors=ecolors, linewidths=0.8)
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ax.add_collection(bodies)
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# X-axis date labels
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tick_idx = range(0, n_days, 5)
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ax.set_xticks(list(tick_idx))
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ax.set_xticklabels([dates[i].strftime("%b %d") for i in tick_idx])
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# Style
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ax.set_xlabel("Date", fontsize=20)
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ax.set_ylabel("Price ($)", fontsize=20)
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ax.set_title("candlestick-basic · seaborn · pyplots.ai", fontsize=24)
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ax.tick_params(axis="both", labelsize=16)
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# Add legend with colorblind-safe colors - positioned outside data area
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legend_elements = [Patch(facecolor=color_up, label="Bullish (Up)"), Patch(facecolor=color_down, label="Bearish (Down)")]
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ax.legend(handles=legend_elements, fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))
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# Subtle grid
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ax.grid(True, alpha=0.3, linestyle="--", axis="y")
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ax.set_title("candlestick-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium", pad=16)
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ax.tick_params(axis="both", labelsize=16, length=0)
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sns.despine(ax=ax)
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ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
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ax.xaxis.grid(False)
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ax.set_axisbelow(True)
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# Set axis limits
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ax.set_xlim(-0.5, n_days - 0.5)
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y_min = df["low"].min()
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y_max = df["high"].max()
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y_padding = (y_max - y_min) * 0.1
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ax.set_ylim(y_min - y_padding, y_max + y_padding)
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# Legend
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legend_handles = [
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Patch(facecolor=up_fill, edgecolor=up_edge, label="Bullish"),
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Patch(facecolor=down_fill, edgecolor=down_edge, label="Bearish"),
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]
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ax.legend(handles=legend_handles, fontsize=14, loc="upper left", framealpha=0.9, edgecolor="#dee2e6")
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# Axis limits
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ax.set_xlim(-0.8, n_days - 0.2)
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y_pad = (df["high"].max() - df["low"].min()) * 0.08
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ax.set_ylim(df["low"].min() - y_pad, df["high"].max() + y_pad)
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/candlestick-basic/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: candlestick-basic
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created: '2025-12-23T10:00:56Z'
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updated: '2025-12-23T10:22:22Z'
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generated_by: claude-opus-4-5-20251101
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updated: '2026-02-24T20:50:21+00:00'
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generated_by: claude-opus-4-6
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workflow_run: 20457529450
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issue: 0
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python_version: 3.13.11
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library_version: 0.13.2
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python_version: "3.14.3"
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library_version: "0.13.2"
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preview_url: https://storage.googleapis.com/pyplots-images/plots/candlestick-basic/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/candlestick-basic/seaborn/plot_thumb.png
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preview_html: null
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quality_score: 90
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quality_score: null
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impl_tags:
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dependencies: []
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techniques:

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