25.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d0a511f",
   "metadata": {},
   "source": [
    "Q1. What is the distinction between a numpy array and a pandas data frame? Is there a way to\n",
    "convert between the two if there is?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118ea3c4",
   "metadata": {},
   "source": [
    "NumPy arrays and Pandas DataFrames are both data structures used in Python for handling and manipulating data, but they have distinct differences in their design and functionality.\n",
    "\n",
    "**NumPy Array:**\n",
    "- A NumPy array is a fundamental data structure provided by the NumPy library.\n",
    "- NumPy arrays are homogeneous, meaning they contain elements of the same data type.\n",
    "- They are designed for numerical and mathematical operations and are highly optimized for numerical computations.\n",
    "- NumPy arrays are suitable for multi-dimensional data, making them efficient for numerical computations involving matrices and vectors.\n",
    "- NumPy does not provide built-in support for column or row labels, making it primarily focused on numerical data.\n",
    "\n",
    "**Pandas DataFrame:**\n",
    "- A Pandas DataFrame is a data structure provided by the Pandas library.\n",
    "- DataFrames are heterogeneous and can contain data of different types in each column.\n",
    "- They are designed for tabular data and are versatile for data manipulation, cleaning, and analysis.\n",
    "- DataFrames provide built-in support for column labels (column names) and row labels (index), making them suitable for handling labeled data.\n",
    "- Pandas DataFrames offer powerful data analysis and manipulation tools, including filtering, grouping, reshaping, and aggregation.\n",
    "\n",
    "**Conversion Between NumPy Array and Pandas DataFrame:**\n",
    "\n",
    "You can convert data between NumPy arrays and Pandas DataFrames using the following methods:\n",
    "\n",
    "1. **From NumPy Array to Pandas DataFrame:**\n",
    "   - You can create a Pandas DataFrame from a NumPy array using the `pandas.DataFrame` constructor.\n",
    "   - This allows you to add column names and labels to your data, which can be beneficial for data analysis and visualization.\n",
    "\n",
    "   ```python\n",
    "   import numpy as np\n",
    "   import pandas as pd\n",
    "\n",
    "   # Create a NumPy array\n",
    "   numpy_array = np.array([[1, 2], [3, 4]])\n",
    "\n",
    "   # Convert to a Pandas DataFrame\n",
    "   df = pd.DataFrame(data=numpy_array, columns=['A', 'B'])\n",
    "\n",
    "   print(df)\n",
    "   ```\n",
    "\n",
    "2. **From Pandas DataFrame to NumPy Array:**\n",
    "   - You can convert a Pandas DataFrame back to a NumPy array using the `to_numpy()` method.\n",
    "   - This allows you to leverage NumPy's numerical computation capabilities if needed.\n",
    "\n",
    "   ```python\n",
    "   import pandas as pd\n",
    "\n",
    "   # Create a Pandas DataFrame\n",
    "   df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n",
    "\n",
    "   # Convert to a NumPy array\n",
    "   numpy_array = df.to_numpy()\n",
    "\n",
    "   print(numpy_array)\n",
    "   ```\n",
    "\n",
    "In summary, NumPy arrays are suitable for numerical computations and multidimensional data, while Pandas DataFrames are designed for more versatile data handling, including labeled data and data analysis. Conversion between the two is straightforward and allows you to leverage the strengths of both libraries for various data manipulation tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f78dce9",
   "metadata": {},
   "source": [
    "Q2. What can go wrong when an user enters in a stock-ticker symbol, and how do you handle it?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b84b7d",
   "metadata": {},
   "source": [
    "When a user enters a stock ticker symbol, several issues or errors can occur, and it's important to handle them gracefully in a stock-related application or website. Here are some common issues that can arise and how you can handle them:\n",
    "\n",
    "1. **Invalid Ticker Symbol:**\n",
    "   - Users may enter an invalid or nonexistent stock ticker symbol.\n",
    "   - Handling: Check whether the entered ticker symbol exists in a database or is recognized by a reliable financial data provider. If it's invalid, provide an error message indicating that the symbol is not recognized.\n",
    "\n",
    "2. **Misspelled Ticker Symbol:**\n",
    "   - Users may make typographical errors when entering ticker symbols.\n",
    "   - Handling: Implement fuzzy string matching or auto-suggestion features to help users find the correct ticker symbol even if they make minor spelling mistakes.\n",
    "\n",
    "3. **No Internet Connection:**\n",
    "   - Users may have a poor or no internet connection when trying to fetch real-time stock data.\n",
    "   - Handling: Display a message indicating that the application requires an internet connection to fetch stock data and provide guidance on how to establish a connection.\n",
    "\n",
    "4. **Data Retrieval Failures:**\n",
    "   - There may be issues with the financial data provider's API, resulting in data retrieval failures.\n",
    "   - Handling: Implement error handling mechanisms to catch API request failures and provide users with informative error messages. You can also implement retry mechanisms with exponential back-off to handle temporary API issues.\n",
    "\n",
    "5. **Rate Limit Exceeded:**\n",
    "   - Some financial data providers impose rate limits on API requests, and users may exceed these limits.\n",
    "   - Handling: Monitor and manage API usage to stay within rate limits. Notify users when they approach or exceed rate limits, and provide guidance on how to proceed.\n",
    "\n",
    "6. **Security Concerns:**\n",
    "   - Users may have concerns about the security of their data when entering stock symbols or other financial information.\n",
    "   - Handling: Ensure that your application follows best practices for data security, including encryption, secure transmission, and data protection. Communicate your security measures to users to build trust.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9aaa58",
   "metadata": {},
   "source": [
    "Q3. Identify some of the plotting techniques that are used to produce a stock-market chart."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b8e70d",
   "metadata": {},
   "source": [
    "Creating stock market charts requires visualization techniques that effectively convey stock price and trading volume data to analysts and investors. Here are some common plotting techniques used to produce stock market charts:\n",
    "\n",
    "1. **Line Chart:** Line charts are commonly used to display the historical closing prices of a stock or an index over a specific time period. Each data point represents the closing price, and connecting these points with lines forms a continuous trend line. Line charts are effective for identifying trends and patterns in stock prices.\n",
    "\n",
    "2. **Candlestick Chart:** Candlestick charts provide more detailed information than line charts. Each candlestick represents a single trading day and includes information about the opening, closing, high, and low prices for that day. Candlestick patterns can reveal price trends and potential reversals.\n",
    "\n",
    "3. **OHLC Chart:** OHLC (Open, High, Low, Close) charts are similar to candlestick charts but are represented as vertical lines with horizontal ticks. They show the price range (high and low) for each trading day along with the opening and closing prices. OHLC charts are useful for technical analysis.\n",
    "\n",
    "4. **Bar Chart:** Bar charts display price data using vertical bars. Each bar represents a trading day, with the height of the bar indicating the price range from the low to the high. A horizontal tick on the left side of the bar represents the opening price, and a horizontal tick on the right side represents the closing price.\n",
    "\n",
    "5. **Volume Chart:** Volume charts plot trading volume for each trading day, typically below a price chart. Bars or columns represent the trading volume, and changes in volume can provide insights into market sentiment and potential price movements.\n",
    "\n",
    "6. **Moving Averages:** Moving averages are used to smooth out price data and identify trends over time. Common types include simple moving averages (SMA) and exponential moving averages (EMA). Plotting moving averages on top of price charts can help traders identify trend changes.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec527f3",
   "metadata": {},
   "source": [
    "Q4. Why is it essential to print a legend on a stock market chart?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203c2f43",
   "metadata": {},
   "source": [
    "Printing a legend on a stock market chart is essential for providing context and clarity to the information presented in the chart. A legend serves several important purposes:\n",
    "\n",
    "1. **Interpretation of Data:** A legend explains the meaning of various elements in the chart, such as different lines, colors, or symbols. In a stock market chart, these elements may represent various data series, indicators, or market events. Users need to know what each element represents to interpret the chart accurately.\n",
    "\n",
    "2. **Identification of Data Series:** Stock market charts often include multiple data series, such as the price of different stocks, market indices, or technical indicators (e.g., moving averages, RSI). A legend helps users identify and distinguish between these data series, preventing confusion.\n",
    "\n",
    "3. **Context for Annotations:** Charts may include annotations, such as arrows, text labels, or shapes, to highlight specific events or observations (e.g., earnings reports, news events, trend changes). The legend can clarify the meaning of these annotations and why they are relevant.\n",
    "\n",
    "4. **Comparative Analysis:** When users want to compare the performance of multiple stocks or assets on the same chart, a legend is crucial. It ensures that users can differentiate between the lines representing each asset and understand which asset corresponds to which line.\n",
    "\n",
    "5. **Customization Options:** Charts often allow users to customize the displayed data or indicators. A legend provides users with information about how to enable or disable specific elements or customize the appearance of the chart.\n",
    "\n",
    "6. **Accessibility and Documentation:** For documentation purposes and future reference, a legend documents the chart's content and settings. This is particularly important in financial analysis, where historical charts and data often play a crucial role.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8a0c9a",
   "metadata": {},
   "source": [
    "Q5. What is the best way to limit the length of a pandas data frame to less than a year?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f8c2c4",
   "metadata": {},
   "source": [
    "To limit the length of a Pandas DataFrame to less than a year, you can filter the DataFrame to include only the rows corresponding to the desired time frame. Here's a step-by-step guide on how to achieve this:\n",
    "\n",
    "Assuming you have a DataFrame with a date or timestamp column, and you want to keep data for less than a year:\n",
    "\n",
    "1. **Import Pandas:** Import the Pandas library if you haven't already.\n",
    "\n",
    "   ```python\n",
    "   import pandas as pd\n",
    "   ```\n",
    "\n",
    "2. **Load or Create DataFrame:** Load your DataFrame or create one with a date or timestamp column. For example:\n",
    "\n",
    "   ```python\n",
    "   # Create a sample DataFrame\n",
    "   data = {'Date': pd.date_range(start='2022-01-01', periods=365, freq='D'),\n",
    "           'Value': range(1, 366)}\n",
    "   df = pd.DataFrame(data)\n",
    "   ```\n",
    "\n",
    "3. **Filter the DataFrame:** Use boolean indexing to filter the DataFrame based on your desired time frame. To keep data for less than a year (e.g., less than 365 days), you can compare the date column with a date one year ago (or the current date if you want to filter by the last year):\n",
    "\n",
    "   ```python\n",
    "   from datetime import datetime, timedelta\n",
    "\n",
    "   # Define the end date (e.g., one year ago from the current date)\n",
    "   end_date = datetime.now() - timedelta(days=365)\n",
    "\n",
    "   # Filter the DataFrame to keep data less than a year\n",
    "   filtered_df = df[df['Date'] > end_date]\n",
    "   ```\n",
    "\n",
    "   In this example, `filtered_df` will contain only the rows with dates within the last year.\n",
    "\n",
    "4. **Optional:** You can reset the index of the filtered DataFrame if needed using the `reset_index()` method:\n",
    "\n",
    "   ```python\n",
    "   filtered_df = filtered_df.reset_index(drop=True)\n",
    "   ```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0001961",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'Date': pd.date_range(start='2022-01-01', periods=365, freq='D'),\n",
    "        'Value': range(1, 366)}\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
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   "execution_count": 2,
   "id": "291a42a6",
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       "      <td>2022-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <td>2022-01-02</td>\n",
       "      <td>2</td>\n",
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       "      <td>2022-01-03</td>\n",
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       "      <td>2022-01-04</td>\n",
       "      <td>4</td>\n",
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       "      <th>360</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>363</td>\n",
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       "      <th>363</th>\n",
       "      <td>2022-12-30</td>\n",
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       "      <th>364</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>365</td>\n",
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       "<p>365 rows × 2 columns</p>\n",
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       "          Date  Value\n",
       "0   2022-01-01      1\n",
       "1   2022-01-02      2\n",
       "2   2022-01-03      3\n",
       "3   2022-01-04      4\n",
       "4   2022-01-05      5\n",
       "..         ...    ...\n",
       "360 2022-12-27    361\n",
       "361 2022-12-28    362\n",
       "362 2022-12-29    363\n",
       "363 2022-12-30    364\n",
       "364 2022-12-31    365\n",
       "\n",
       "[365 rows x 2 columns]"
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   "cell_type": "code",
   "execution_count": 3,
   "id": "54733ea6",
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   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Define the end date (e.g., one year ago from the current date)\n",
    "end_date = datetime.now() - timedelta(days=365)\n",
    "\n",
    "# Filter the DataFrame to keep data less than a year\n",
    "filtered_df = df[df['Date'] > end_date]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb23e3b3",
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       "      <th>255</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2022-09-16</td>\n",
       "      <td>259</td>\n",
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       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2022-09-17</td>\n",
       "      <td>260</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>360</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>361</td>\n",
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       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>363</td>\n",
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       "      <th>363</th>\n",
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       "      <th>364</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>365</td>\n",
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       "<p>110 rows × 2 columns</p>\n",
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       "          Date  Value\n",
       "255 2022-09-13    256\n",
       "256 2022-09-14    257\n",
       "257 2022-09-15    258\n",
       "258 2022-09-16    259\n",
       "259 2022-09-17    260\n",
       "..         ...    ...\n",
       "360 2022-12-27    361\n",
       "361 2022-12-28    362\n",
       "362 2022-12-29    363\n",
       "363 2022-12-30    364\n",
       "364 2022-12-31    365\n",
       "\n",
       "[110 rows x 2 columns]"
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  {
   "cell_type": "markdown",
   "id": "edae1f14",
   "metadata": {},
   "source": [
    "Q6. What is the definition of a 180-day moving average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72238e06",
   "metadata": {},
   "outputs": [],
   "source": []
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