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\n\u0633\u0647\u0648\u0644\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647: \u0633\u06cc\u0646\u062a\u06a9\u0633 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648\u0627\u0636\u062d \u0648 \u0634\u0647\u0648\u062f\u06cc \u0627\u0633\u062a \u0648 \u0628\u0631\u0627\u06cc \u0645\u0628\u062a\u062f\u06cc\u0627\u0646 \u0642\u0627\u0628\u0644 \u062f\u0633\u062a\u0631\u0633\u06cc \u0627\u0633\u062a.<\/p>\n
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\n\u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc \u0627\u0646\u062c\u0645\u0646: \u06cc\u06a9 \u062c\u0627\u0645\u0639\u0647 \u0628\u0632\u0631\u06af \u0628\u0647 \u0645\u0639\u0646\u0627\u06cc \u0645\u0646\u0627\u0628\u0639\u060c \u0622\u0645\u0648\u0632\u0634 \u0647\u0627 \u0648 \u0627\u0646\u062c\u0645\u0646 \u0647\u0627\u06cc \u0641\u0631\u0627\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u06a9\u0645\u06a9 \u0627\u0633\u062a.<\/p>\n
\u0634\u0631\u0648\u0639 \u06a9\u0627\u0631 \u0628\u0627 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0648 Matplotlib \ud83d\udee0\ufe0f<\/p>\n
\u0645\u0631\u062d\u0644\u0647 1: \u0646\u0635\u0628 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632<\/p>\n
\u0642\u0628\u0644 \u0627\u0632 \u0634\u0631\u0648\u0639 \u06a9\u062f\u0646\u0648\u06cc\u0633\u06cc\u060c \u0645\u0637\u0645\u0626\u0646 \u0634\u0648\u06cc\u062f \u06a9\u0647 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0644\u0627\u0632\u0645 \u0631\u0627 \u0646\u0635\u0628 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f. \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f Pandas \u0648 Matplotlib \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 pip \u0646\u0635\u0628 \u06a9\u0646\u06cc\u062f:<\/p>\n
pip install pandas matplotlib<\/p>\n
\u0645\u0631\u062d\u0644\u0647 2: \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0632 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \ud83d\udce5
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Date,Sales
\n2024-01-01,100
\n2024-01-02,150
\n2024-01-03,200
\n2024-01-04,250
\n2024-01-05,300
\n2024-01-06,350
\n2024-01-07,400<\/p>\n
\u0645\u0631\u062d\u0644\u0647 3: \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0627 \u067e\u0627\u0646\u062f\u0627\u0647\u0627
\n\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0646\u0648\u06cc\u0633\u06cc\u0645 \u062a\u0627 \u0627\u06cc\u0646 \u0641\u0627\u06cc\u0644 CSV \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0686\u0646\u062f \u062a\u062d\u0644\u06cc\u0644 \u0627\u0648\u0644\u06cc\u0647 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645. \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u0628\u0627 \u0646\u0627\u0645 data_analysis.py \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u062f:<\/p>\n
import pandas as pd<\/p>\n
# Load the dataset
\ndata = pd.read_csv(‘sales_data.csv’)<\/p>\n
# Display the first few rows of the dataset
\nprint(“Data Overview:”)
\nprint(data.head())<\/p>\n
# Calculate total sales
\ntotal_sales = data[‘Sales’].sum()
\nprint(f”\\nTotal Sales: {total_sales}”)<\/p>\n
# Calculate average sales
\naverage_sales = data[‘Sales’].mean()
\nprint(f”Average Daily Sales: {average_sales:.2f}”)<\/p>\n
\u062a\u0648\u0636\u06cc\u062d:<\/p>\n
\u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627: \u062a\u0627\u0628\u0639 pd.read_csv() \u0641\u0627\u06cc\u0644 CSV \u0631\u0627 \u062f\u0631 \u06cc\u06a9 DataFrame \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u062f.
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\u0645\u0631\u062d\u0644\u0647 4: \u062a\u062c\u0633\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0627 Matplotlib \ud83d\udcc8<\/p>\n
\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0641\u0631\u0648\u0634 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Matplotlib \u062a\u062c\u0633\u0645 \u06a9\u0646\u06cc\u0645. \u0641\u0627\u06cc\u0644 data_analysis.py \u062e\u0648\u062f \u0631\u0627 \u0628\u0627 \u06a9\u062f \u0632\u06cc\u0631 \u06af\u0633\u062a\u0631\u0634 \u062f\u0647\u06cc\u062f:<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
# Plotting the sales data
\nplt.figure(figsize=(10, 5))
\nplt.plot(data[‘Date’], data[‘Sales’], marker=’o’, linestyle=’-‘, color=’b’)
\nplt.title(‘Daily Sales Over Time’)
\nplt.xlabel(‘Date’)
\nplt.ylabel(‘Sales’)
\nplt.xticks(rotation=45)
\nplt.grid()
\nplt.tight_layout() # Adjust layout to prevent clipping of labels
\nplt.show()<\/p>\n
\u062a\u0648\u0636\u06cc\u062d:<\/p>\n
\u0631\u0633\u0645: \u0645\u0627 \u06cc\u06a9 \u0637\u0631\u062d \u062e\u0637\u06cc \u0628\u0631\u0627\u06cc \u062a\u062c\u0633\u0645 \u0641\u0631\u0648\u0634 \u0631\u0648\u0632\u0627\u0646\u0647 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.
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\u0646\u062a\u06cc\u062c\u0647 \u06af\u06cc\u0631\u06cc: \u0628\u0627\u0632 \u06a9\u0631\u062f\u0646 \u0628\u06cc\u0646\u0634 \u0647\u0627 \u0628\u0627 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627 \ud83d\udcca\u2728<\/p>\n
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\u0645\u0631\u0627\u062d\u0644 \u0628\u0639\u062f\u06cc:<\/p>\n
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\n\u0633\u0647\u0648\u0644\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647: \u0633\u06cc\u0646\u062a\u06a9\u0633 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648\u0627\u0636\u062d \u0648 \u0634\u0647\u0648\u062f\u06cc \u0627\u0633\u062a \u0648 \u0628\u0631\u0627\u06cc \u0645\u0628\u062a\u062f\u06cc\u0627\u0646 \u0642\u0627\u0628\u0644 \u062f\u0633\u062a\u0631\u0633\u06cc \u0627\u0633\u062a.<\/p>\n\n
\n
pip install <\/span>pandas matplotlib\n<\/code><\/pre>\n<\/div>\n\n
\n\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u0641\u0627\u06cc\u0644 CSV \u0628\u0627 \u0646\u0627\u0645 sales_data.csv \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062d\u0627\u0648\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0641\u0631\u0648\u0634 \u062e\u06cc\u0627\u0644\u06cc \u0627\u0633\u062a:\n<\/li>\n<\/ul>\nDate,Sales\n2024-01-01,100\n2024-01-02,150\n2024-01-03,200\n2024-01-04,250\n2024-01-05,300\n2024-01-06,350\n2024-01-07,400\n<\/code><\/pre>\n<\/div>\n\n
\n\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0646\u0648\u06cc\u0633\u06cc\u0645 \u062a\u0627 \u0627\u06cc\u0646 \u0641\u0627\u06cc\u0644 CSV \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0686\u0646\u062f \u062a\u062d\u0644\u06cc\u0644 \u0627\u0648\u0644\u06cc\u0647 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645. \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u0628\u0627 \u0646\u0627\u0645 data_analysis.py \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u062f:\n<\/li>\n<\/ul>\nimport<\/span> pandas<\/span> as<\/span> pd<\/span>\n\n# Load the dataset\n<\/span>data<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>'<\/span>sales_data.csv<\/span>'<\/span>)<\/span>\n\n# Display the first few rows of the dataset\n<\/span>print<\/span>(<\/span>\"<\/span>Data Overview:<\/span>\"<\/span>)<\/span>\nprint<\/span>(<\/span>data<\/span>.<\/span>head<\/span>())<\/span>\n\n# Calculate total sales\n<\/span>total_sales<\/span> =<\/span> data<\/span>[<\/span>'<\/span>Sales<\/span>'<\/span>].<\/span>sum<\/span>()<\/span>\nprint<\/span>(<\/span>f<\/span>\"<\/span>\\n<\/span>Total Sales: <\/span>{<\/span>total_sales<\/span>}<\/span>\"<\/span>)<\/span>\n\n# Calculate average sales\n<\/span>average_sales<\/span> =<\/span> data<\/span>[<\/span>'<\/span>Sales<\/span>'<\/span>].<\/span>mean<\/span>()<\/span>\nprint<\/span>(<\/span>f<\/span>\"<\/span>Average Daily Sales: <\/span>{<\/span>average_sales<\/span>:<\/span>.<\/span>2<\/span>f<\/span>}<\/span>\"<\/span>)<\/span>\n<\/code><\/pre>\n<\/div>\n\n
import<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\n\n# Plotting the sales data\n<\/span>plt<\/span>.<\/span>figure<\/span>(<\/span>figsize<\/span>=<\/span>(<\/span>10<\/span>,<\/span> 5<\/span>))<\/span>\nplt<\/span>.<\/span>plot<\/span>(<\/span>data<\/span>[<\/span>'<\/span>Date<\/span>'<\/span>],<\/span> data<\/span>[<\/span>'<\/span>Sales<\/span>'<\/span>],<\/span> marker<\/span>=<\/span>'<\/span>o<\/span>'<\/span>,<\/span> linestyle<\/span>=<\/span>'<\/span>-<\/span>'<\/span>,<\/span> color<\/span>=<\/span>'<\/span>b<\/span>'<\/span>)<\/span>\nplt<\/span>.<\/span>title<\/span>(<\/span>'<\/span>Daily Sales Over Time<\/span>'<\/span>)<\/span>\nplt<\/span>.<\/span>xlabel<\/span>(<\/span>'<\/span>Date<\/span>'<\/span>)<\/span>\nplt<\/span>.<\/span>ylabel<\/span>(<\/span>'<\/span>Sales<\/span>'<\/span>)<\/span>\nplt<\/span>.<\/span>xticks<\/span>(<\/span>rotation<\/span>=<\/span>45<\/span>)<\/span>\nplt<\/span>.<\/span>grid<\/span>()<\/span>\nplt<\/span>.<\/span>tight_layout<\/span>()<\/span> # Adjust layout to prevent clipping of labels\n<\/span>plt<\/span>.<\/span>show<\/span>()<\/span>\n\n<\/code><\/pre>\n<\/div>\n\n
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\n<\/h1>\n","protected":false},"excerpt":{"rendered":"