Getting started with Python data analysis using public data

1. Learn how to use Anaconda Jupyter Notebook

2. (Optional) Using the table of contents feature with Jupyter nbextensions

3. Source code and data download location

5. [2/20] Previewing two completely different datasets

6. [3/20] Load data and preview, find out the number of rows and columns

7. [4/20] Data preprocessing - View missing values and change the sales price data type to numerical format

8. [5/20] Understanding the difference between character and numeric data when looking at descriptive statistics

10. [7/20] Grouping data and performing calculations with groupby

11. [8/20] Understanding data operations with pivot_table and the difference with groupby

12. [9/20] Expressing grouped data in various forms using Pandas' visualization function

13. [10/20] Understanding the 5 figures of boxplot and visualizing the selling price

14. [11/20] Visualizing with seaborn - Drawing line graphs and subplots with lineplot and relplot

15. [12/20] Drawing box and violinplot with seaborn

16. [13/20] Understanding the differences between scatterplot, regplot, lmplot, and swarmplot through visualization of distribution price data

17. [14/20] Visualizing and drawing subplots with distplot, ridgeplot, and pairplot

19. [16/20] Separate year and month using function and pandas apply to create a new column

20. [17/20] Merge two data frames into one using pandas' concat

21. [18/20] Expressing the results calculated with pivot_table as a heatmap according to the number of values

22. [19/20] Visualize merged data from 2013 to recent data

23. [20/20] Visualizing and organizing the sales prices by region

24. Source code and data download location

25. [1/15] Introduction to commercial (commercial area) information dataset and loading required libraries

26. [2/15] Load data and view missing values

27. [3/15] Visualizing missing values with the missingno library

28. [4/15] Understanding the differences between retrieving row and column values with .loc and .iloc

29. [5/15] View descriptive statistics for univariate numeric data - describe

30. [6/15] Visualizing Univariate Numerical Variables - Using distplot

32. [8/15] Analyzing univariate categorical variables - describe and value_counts

34. [10/15] Create a subset to compare the types of schools

35. [11/15] Are there many entrance exam prep schools in Daechi-dong and Mok-dong?

36. [12/15] Groupby operation and visualization - Using unstack

37. [13/15] Easily draw subplots of categorical data - catplot

38. [14/15] Handling latitude and longitude data in various ways using scatterplot

39. [15/15] Detailing the map with folium

40. Source code and data download location

41. [1/10] Loading libraries and setting up basic settings for franchise analysis

42. [2/10] Load data, subset only the necessary data, and save as a csv file

43. [3/10] Handling Text Data - Retrieving Baskin-Robbins and Dunkin' Donuts Data Only

44. [4/10] Visualizing with countplot, scatterplot, and jointplot

45. [5/10] Marking Baskin-Robbins and Dunkin' Donuts stores on the map with folium marker

46. [6/10] Drawing MarkerCluster map with folium

48. [8/10] Visualizing Paris Baguette and Tous Les Jours Data

49. [9/10] Expressing store location with CircleMarker in folium and changing style with tiles

50. [10/10] Expressing store density by location using MarkerCluster and Heatmap

51. Source code and data download location

52. [1/9] Will the Ediya store be located near Starbucks?

53. [2/9] Starbucks, Ediya Text Data Preprocessing and Brand Name Creation

54. [3/9] Data visualization with seaborn - Drawing kde plot with jointplot

55. [4/9] Compare the number of stores by brand using gropby and pivot_table

56. [5/9] Understanding the difference in visualization with groupby and pivot_table return values

58. [7/9] Understanding Geo JSON values and drawing choropleths

59. [8/9] Find the average of the latitude and longitude of each district using pivot_table and merge them using merge

60. [9/9] Draw the size of the CircleMarker to reflect the number of stores

61. Source code and data download location

62. [1/21] Introduction to National Urban Park Data Analysis

63. [2/21] Setting font and style with seaborn

64. [Appendix] What if you are suffering from Korean fonts in pandas-profiling?! How to set Korean fonts

65. [3/21] From technical statistics to report generation in one line of code - Pandas Profiling

66. [4/21] Missing value visualization - Drawing the missingno matrix with seaborn

67. [5/21] Handling Missing Values - Replacing Missing Values in Road Name Addresses with Street Addresses

68. [6/21] Creating derived variables for city and county names using addresses

69. [7/21] Finding outliers and error values in numerical data through latitude and longitude

70. [8/21] Changing data types and creating derived variables through dates

71. [9/21] Regular expressions for text data preprocessing

72. [10/21] Preprocessing text entered in different formats with regular expressions and counting frequency

73. [11/21] Visualizing the frequency of sports facilities with word clouds

74. [12/21] Creating a text preprocessing function using regular expressions

75. [13/21] Counting text frequency through keyword extraction

76. [14/21] Information Masking - Masking part of phone number

77. [15/21] Masking email and car registration numbers with regular expressions

78. [16/21] Summarizing numeric vs categorical variables and operating on categorical data through crosstab

79. [17/21] Finding the frequency and ratio of parks by city/province

80. [18/21] Visualizing National Urban Parks with Scatterplot

81. [19/21] Adding and removing lists and easily drawing subplots with pairplot

82. [20/21] Calculating the number and area of parks by city using pivot tables

83. [21/21] Find parks around me and show them on a map