corvo7/Data_Analysis
1
1import streamlit as st2 3# Custom CSS to style the page with 3D features4st.markdown("""5 <style>6 .main {7 background-color: #ffffff;8 }9 .center-image {10 display: block;11 margin-left: auto;12 margin-right: auto;13 width: 60%;14 box-shadow: 10px 10px 30px rgba(0, 0, 0, 0.3);15 border-radius: 15px;16 margin-top: 20px;17 }18 .content {19 color: #333333;20 padding: 20px;21 font-size: 18px;22 box-shadow: 5px 5px 15px rgba(0, 0, 0, 0.2);23 border-radius: 15px;24 background: #f8f9fa;25 margin-left: 20px;26 margin-top: 20px;27 }28 .button {29 font-size: 20px;30 margin-bottom: 20px;31 padding: 15px;32 box-shadow: 3px 3px 10px rgba(0, 0, 0, 0.2);33 border-radius: 10px;34 background: #007bff;35 color: white;36 transition: transform 0.2s, background 0.2s;37 border: none;38 width: 100%;39 text-align: left;40 }41 .button:hover {42 box-shadow: 3px 3px 15px rgba(0, 0, 0, 0.3);43 transform: scale(1.05);44 cursor: pointer;45 background: #0056b3;46 }47 .button:focus {48 outline: none;49 box-shadow: 6px 6px 15px rgba(0, 0, 0, 0.3);50 transform: scale(1.05);51 background: linear-gradient(to bottom, #003580, #002060);52 }53 </style>54 """, unsafe_allow_html=True)55 56# Page title57st.title("Data Analysis With Python Roadmap")58 59# Center image at the top60st.image("images/roadmap.jpg", use_column_width='always')61 62# Two-column layout63col1, col2 = st.columns([1, 2])64 65# Left column with the buttons66with col1:67 st.header("Topics")68 69 selection = None70 if st.button("Basic Python", key="basic_python"):71 selection = "Basic Python"72 if st.button("Intermediate Python", key="intermediate_python"):73 selection = "Intermediate Python"74 if st.button("Descriptive Statistics", key="descriptive_statistics"):75 selection = "Descriptive Statistics"76 if st.button("NumPy", key="numpy"):77 selection = "NumPy"78 if st.button("Pandas", key="pandas"):79 selection = "Pandas"80 if st.button("Matplotlib", key="matplotlib"):81 selection = "Matplotlib"82 if st.button("Seaborn", key="seaborn"):83 selection = "Seaborn"84 if st.button("Inferential Statistics", key="inferential_statistics"):85 selection = "Inferential Statistics"86 87# Right column with the topic description88with col2:89 if selection:90 if selection == "Basic Python":91 st.image("images/python_logo.png", width=50)92 st.markdown("""93 <div class='content'>94 <b>Basic Python:</b>95 <p>Basic Python covers the fundamental aspects of the Python programming language.</p>96 97 <b>Subtopics:</b>98 <ul>99 <li>Syntax: Understanding the basic syntax and structure of Python code.</li>100 <li>Data Types: Working with strings, lists, dictionaries, and tuples.</li>101 <li>Control Flow: Using loops, conditionals, and functions.</li>102 <li>File Handling: Reading from and writing to files.</li>103 </ul>104 105 <b>Example:</b>106 <p>Writing simple programs to automate repetitive tasks, such as renaming files in bulk.</p>107 </div>108 """, unsafe_allow_html=True)109 110 elif selection == "Intermediate Python":111 st.image("images/python_logo.png", width=50)112 st.markdown("""113 <div class='content'>114 <b>Intermediate Python:</b>115 <p>Intermediate Python includes more advanced features of Python programming.</p>116 117 <b>Subtopics:</b>118 <ul>119 <li>Modules and Packages: Importing and organizing code into modules.</li>120 <li>List Comprehensions: Creating lists in a more readable way.</li>121 <li>Error Handling: Using try, except blocks to handle errors.</li>122 <li>Classes and Objects: Understanding object-oriented programming concepts.</li>123 </ul>124 125 <b>Example:</b>126 <p>Building reusable code modules and handling exceptions in data processing scripts.</p>127 </div>128 """, unsafe_allow_html=True)129 130 elif selection == "Descriptive Statistics":131 st.image("images/statistics_logo.png", width=50)132 st.markdown("""133 <div class='content'>134 <b>Descriptive Statistics:</b>135 <p>Descriptive statistics summarize and describe the main features of a dataset.</p>136 137 <b>Subtopics:</b>138 <ul>139 <li>Central Tendency: Mean, median, mode.</li>140 <li>Dispersion: Variance, standard deviation, range.</li>141 <li>Distribution: Quartiles, percentiles, histograms.</li>142 </ul>143 144 <b>Example:</b>145 <p>Summarizing sales data to understand the average sales per month and the variability in sales.</p>146 </div>147 """, unsafe_allow_html=True)148 149 elif selection == "NumPy":150 st.image("images/numpy_logo.png", width=50)151 st.markdown("""152 <div class='content'>153 <b>NumPy:</b>154 <p>NumPy is a fundamental package for numerical computing in Python.</p>155 156 <b>Subtopics:</b>157 <ul>158 <li>Arrays: Creating and manipulating arrays.</li>159 <li>Mathematical Operations: Performing element-wise and matrix operations.</li>160 <li>Statistical Functions: Using built-in functions for analysis.</li>161 <li>Data Transformation: Reshaping and slicing arrays.</li>162 </ul>163 164 <b>Example:</b>165 <p>Performing fast and efficient calculations on large datasets, such as computing the sum of all elements in an array.</p>166 </div>167 """, unsafe_allow_html=True)168 169 elif selection == "Pandas":170 st.image("images/pandas_logo.png", width=100)171 st.markdown("""172 <div class='content'>173 <b>Pandas:</b>174 <p>Pandas is a powerful library for data manipulation and analysis in Python.</p>175 176 <b>Subtopics:</b>177 <ul>178 <li>DataFrames: Creating and manipulating DataFrames.</li>179 <li>Data Cleaning: Handling missing values and duplicates.</li>180 <li>Data Transformation: Merging, joining, and concatenating DataFrames.</li>181 <li>Data Analysis: Grouping and aggregating data.</li>182 </ul>183 184 <b>Example:</b>185 <p>Cleaning and analyzing sales data from different regions to find total sales per product category.</p>186 </div>187 """, unsafe_allow_html=True)188 189 elif selection == "Matplotlib":190 st.image("images/matplotlib_logo.png", width=100)191 st.markdown("""192 <div class='content'>193 <b>Matplotlib:</b>194 <p>Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python.</p>195 196 <b>Subtopics:</b>197 <ul>198 <li>Basic Plots: Creating line, bar, and scatter plots.</li>199 <li>Customization: Customizing plots with titles, labels, and legends.</li>200 <li>Subplots: Creating multiple plots in a single figure.</li>201 </ul>202 203 <b>Example:</b>204 <p>Visualizing sales trends over time with a line chart and customizing it to include titles and labels.</p>205 </div>206 """, unsafe_allow_html=True)207 208 elif selection == "Seaborn":209 st.image("images/seaborn_logo.png", width=100)210 st.markdown("""211 <div class='content'>212 <b>Seaborn:</b>213 <p>Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.</p>214 215 <b>Subtopics:</b>216 <ul>217 <li>Statistical Plots: Creating plots like histograms, box plots, and violin plots.</li>218 <li>Customization: Advanced customization of plots.</li>219 <li>Integration: Seamless integration with pandas DataFrames.</li>220 </ul>221 222 <b>Example:</b>223 <p>Creating a box plot to visualize the distribution of exam scores across different classes.</p>224 </div>225 """, unsafe_allow_html=True)226 227 elif selection == "Inferential Statistics":228 st.image("images/statistics_logo.png", width=50)229 st.markdown("""230 <div class='content'>231 <b>Inferential Statistics:</b>232 <p>Inferential statistics allow us to make predictions or inferences about a population based on a sample of data.</p>233 234 <b>Subtopics:</b>235 <ul>236 <li>Hypothesis Testing: Determining the validity of assumptions.</li>237 <li>Confidence Intervals: Estimating population parameters.</li>238 <li>Regression Analysis: Modeling relationships between variables.</li>239 <li>ANOVA and Chi-Square Tests: Comparing group means and categorical variables.</li>240 </ul>241 242 <b>Example:</b>243 <p>Using regression analysis to predict future sales based on past data trends and conducting hypothesis tests to determine if a new marketing strategy significantly impacts sales.</p>244 </div>245 """, unsafe_allow_html=True)246 247