Spaces:
Sleeping
Sleeping
File size: 6,250 Bytes
d18f851 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """
Chart Customizer - Let users choose chart types
"""
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
class ChartCustomizer:
def __init__(self, df):
self.df = df
def get_available_charts(self):
"""Return available chart types based on data"""
charts = []
if len(self.df.select_dtypes(include=['number']).columns) > 0:
charts.append('📊 Histogram')
charts.append('📈 Line Chart')
charts.append('📉 Scatter Plot')
charts.append('📦 Box Plot')
if len(self.df.select_dtypes(include=['object']).columns) > 0:
charts.append('🥧 Bar Chart')
charts.append('🍩 Pie Chart')
if len(self.df.select_dtypes(include=['datetime64']).columns) > 0:
charts.append('📅 Time Series')
charts.append('🔥 Heatmap')
return charts
def create_chart(self, chart_type, x_col, y_col=None, color_col=None, title=None):
"""Create customized chart"""
if title is None:
title = f"{chart_type}: {x_col}"
if y_col:
title += f" vs {y_col}"
# Histogram
if 'Histogram' in chart_type:
fig = px.histogram(
self.df, x=x_col,
title=title,
color=color_col if color_col else None,
nbins=30,
color_discrete_sequence=px.colors.sequential.Plasma
)
# Bar Chart
elif 'Bar Chart' in chart_type:
if y_col and y_col in self.df.columns:
# Grouped bar chart
agg_data = self.df.groupby(x_col)[y_col].mean().reset_index()
fig = px.bar(
agg_data, x=x_col, y=y_col,
title=title,
color=color_col if color_col else None,
color_discrete_sequence=px.colors.qualitative.Set2
)
else:
# Count bar chart
counts = self.df[x_col].value_counts().head(20).reset_index()
counts.columns = [x_col, 'count']
fig = px.bar(
counts, x=x_col, y='count',
title=f"Count of {x_col}",
color_discrete_sequence=['#2E86AB']
)
# Line Chart
elif 'Line Chart' in chart_type:
if y_col and y_col in self.df.columns:
fig = px.line(
self.df, x=x_col, y=y_col,
title=title,
color=color_col if color_col else None,
markers=True
)
else:
fig = px.line(
self.df, x=x_col,
title=title,
markers=True
)
# Scatter Plot (without trendline to avoid statsmodels)
elif 'Scatter' in chart_type:
if y_col and y_col in self.df.columns:
fig = px.scatter(
self.df, x=x_col, y=y_col,
title=title,
color=color_col if color_col else None,
size=y_col if y_col else None,
hover_data=[x_col, y_col] if y_col else [x_col]
# Removed trendline to avoid statsmodels
)
else:
fig = px.scatter(
self.df, x=x_col, y=x_col,
title=title,
color=color_col if color_col else None
)
# Box Plot
elif 'Box' in chart_type:
if y_col and y_col in self.df.columns:
fig = px.box(
self.df, x=x_col, y=y_col,
title=title,
color=color_col if color_col else None,
points="all"
)
else:
fig = px.box(
self.df, y=x_col,
title=f"Box Plot of {x_col}",
points="all"
)
# Pie Chart
elif 'Pie' in chart_type:
counts = self.df[x_col].value_counts().head(10).reset_index()
counts.columns = [x_col, 'count']
fig = px.pie(
counts, values='count', names=x_col,
title=f"Distribution of {x_col}",
hole=0.3
)
# Heatmap
elif 'Heatmap' in chart_type:
numeric_cols = self.df.select_dtypes(include=['number']).columns
if len(numeric_cols) > 1:
corr = self.df[numeric_cols].corr()
fig = px.imshow(
corr,
text_auto='.2f',
aspect='auto',
color_continuous_scale='RdBu',
title="Correlation Heatmap"
)
else:
return None
# Time Series
elif 'Time Series' in chart_type:
date_cols = self.df.select_dtypes(include=['datetime64']).columns
if len(date_cols) > 0:
date_col = date_cols[0]
if y_col and y_col in self.df.columns:
time_data = self.df.groupby(date_col)[y_col].mean().reset_index()
fig = px.line(
time_data, x=date_col, y=y_col,
title=f"{y_col} Over Time",
markers=True
)
else:
fig = None
else:
fig = None
else:
fig = None
if fig:
# Apply common styling
fig.update_layout(
template='plotly_white',
height=500,
title_font_size=16,
title_x=0.5
)
return fig |