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Python code example - How to create infographics using python and matplotlib, pytrends sample code and tutorial on Google Trends visualization
By Guest on 12th December 2022 01:07:59 AM | Syntax: PYTHON | Views: 310


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  1. To create infographics using Python, you can use a library called Matplotlib. This library allows you to create various types of graphs, charts, and other visualizations using Python. To use Matplotlib, you will need to install it first. This can be done using the pip package manager by running the following command:
  2.  
  3. pip install matplotlib
  4. Once you have installed Matplotlib, you can start creating infographics by importing the library and using its various functions. For example, to create a bar chart, you can use the bar() function, which takes various parameters such as the data to be plotted, the labels for the axes, and the colors to be used. Here is an example of how you can use the bar() function to create a simple bar chart:
  5.  
  6. import matplotlib.pyplot as plt
  7.  
  8. data = [1, 2, 3, 4, 5]
  9.  
  10. plt.bar(data)
  11.  
  12. plt.show()
  13. This code will create a bar chart with the given data and display it on the screen. You can then customize the chart by adding titles, labels, and other elements, and save it to an image file using the savefig() function.
  14.  
  15. Overall, creating infographics using Python and Matplotlib can be a powerful way to visualize data and communicate information in a clear and engaging way. By using the various functions and options provided by the library, you can create a wide range of infographics to suit your needs.
  16.  
  17. https://www.youtube.com/watch?v=D4VlmL3G4_o
  18.  
  19. Here is an example of using Matplotlib to plot data from Google Trends:
  20.  
  21. import matplotlib.pyplot as plt
  22. from pytrends.request import TrendReq
  23.  
  24. # Create a new instance of the TrendReq class
  25. pytrend = TrendReq()
  26.  
  27. # Enter the keywords you want to track
  28. keywords = ['dog', 'cat']
  29.  
  30. # Set the timeframe you want to track the data for
  31. timeframe = 'today 5-y'
  32.  
  33. # Create a dataframe with the trends data
  34. trends_df = pytrend.get_historical_interest(keywords, year_start=2018, month_start=1, day_start=1, hour_start=0, year_end=2018, month_end=2, day_end=1, hour_end=0, cat=0, geo='', gprop='', sleep=60)
  35.  
  36. # Create a line plot with the data
  37. plt.plot(trends_df)
  38.  
  39. # Add a title and axis labels
  40. plt.title('Google Trends for dog and cat')
  41. plt.xlabel('Time')
  42. plt.ylabel('Interest')
  43.  
  44. # Show the plot
  45. plt.show()
  46.  
  47.  
  48. This example creates a line plot showing the historical interest in the keywords "dog" and "cat" over the past 5 years. The x-axis represents time, and the y-axis represents the level of interest in the keywords. The plot is created using the plot() method from Matplotlib, and the data is obtained using the get_historical_interest() method from the pytrends library.
















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