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CHARLS_meteorology.py

CHARLS_meteorology.py 7.7 KB
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  1. import pandas as pd
    2. 

  2. 

  3. def sunlight(CHARLS_data):
    4. 

  4.     years = [2011, 2013,2015, 2018, 2020]
    5. 

  5.     for year in years:
    6. 

  6.         #读取日照数据
    7. 

  7.         sunlight_data_last = pd.read_excel(f"气象数据/2000-2020年按年逐年日照/【{year-1}年】逐年日照.xlsx")
    8. 

  8.         sunlight_data_last = sunlight_data_last[["城市", "累积日照"]]
    9. 

  9.         sunlight_data_last = sunlight_data_last.rename(columns={"累积日照":"last_sunlight"})
    10. 

  10.         sunlight_data_before_last = pd.read_excel(f"气象数据/2000-2020年按年逐年日照/【{year-2}年】逐年日照.xlsx")
    11. 

  11.         sunlight_data_before_last = sunlight_data_before_last[["城市", "累积日照"]]
    12. 

  12.         sunlight_data_before_last = sunlight_data_before_last.rename(columns={"累积日照":"before_sunlight"})
    13. 

  13.         #开始筛选出year的数据
    14. 

  14.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    15. 

  15.         #合并日照
    16. 

  16.         table_merge = pd.merge(CHARLS_data_year, sunlight_data_last, left_on="city", right_on="城市", how='left')
    17. 

  17.         table_merge = pd.merge(table_merge, sunlight_data_before_last, left_on="city", right_on="城市", how='left')
    18. 

  18.         #更新CHARLS表
    19. 

  19.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_sunlight'] = table_merge['last_sunlight'].values
    20. 

  20.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_sunlight'] = table_merge['before_sunlight'].values
    21. 

  21.         CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    22. 

  22.         print(year)
    23. 

  23. 

  24. def wind(CHARLS_data):
    25. 

  25.     years = [2011, 2013,2015, 2018, 2020]
    26. 

  26.     for year in years:
    27. 

  27.         #读取日照数据
    28. 

  28.         wind_data_last = pd.read_excel(f"气象数据/2000-2020年按年逐年风速/【{year-1}年】逐年风速.xlsx")
    29. 

  29.         wind_data_last = wind_data_last[["城市", "平均风速"]]
    30. 

  30.         wind_data_last = wind_data_last.rename(columns={"平均风速":"last_wind"})
    31. 

  31.         wind_data_before_last = pd.read_excel(f"气象数据/2000-2020年按年逐年风速/【{year-2}年】逐年风速.xlsx")
    32. 

  32.         wind_data_before_last = wind_data_before_last[["城市", "平均风速"]]
    33. 

  33.         wind_data_before_last = wind_data_before_last.rename(columns={"平均风速":"before_wind"})
    34. 

  34.         #开始筛选出year的数据
    35. 

  35.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    36. 

  36.         #合并日照
    37. 

  37.         table_merge = pd.merge(CHARLS_data_year, wind_data_last, left_on="city", right_on="城市", how='left')
    38. 

  38.         table_merge = pd.merge(table_merge, wind_data_before_last, left_on="city", right_on="城市", how='left')
    39. 

  39.         #更新CHARLS表
    40. 

  40.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_wind'] = table_merge['last_wind'].values
    41. 

  41.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_wind'] = table_merge['before_wind'].values
    42. 

  42.         CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    43. 

  43.         print(year)
    44. 

  44. 

  45. def rain(CHARLS_data):
    46. 

  46.     years = [2011, 2013,2015, 2018, 2020]
    47. 

  47.     for year in years:
    48. 

  48.         #读取日照数据
    49. 

  49.         rain_data_last = pd.read_excel(f"气象数据/2000-2020年按年逐年降水/【{year-1}年】逐年降水.xlsx")
    50. 

  50.         rain_data_last = rain_data_last[["城市", "累积降水"]]
    51. 

  51.         rain_data_last = rain_data_last.rename(columns={"累积降水":"last_rain"})
    52. 

  52.         rain_data_before_last = pd.read_excel(f"气象数据/2000-2020年按年逐年降水/【{year-2}年】逐年降水.xlsx")
    53. 

  53.         rain_data_before_last = rain_data_before_last[["城市", "累积降水"]]
    54. 

  54.         rain_data_before_last = rain_data_before_last.rename(columns={"累积降水":"before_rain"})
    55. 

  55.         #开始筛选出year的数据
    56. 

  56.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    57. 

  57.         #合并日照
    58. 

  58.         table_merge = pd.merge(CHARLS_data_year, rain_data_last, left_on="city", right_on="城市", how='left')
    59. 

  59.         table_merge = pd.merge(table_merge, rain_data_before_last, left_on="city", right_on="城市", how='left')
    60. 

  60.         #更新CHARLS表
    61. 

  61.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_rain'] = table_merge['last_rain'].values
    62. 

  62.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_rain'] = table_merge['before_rain'].values
    63. 

  63.         CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    64. 

  64.         print(year)
    65. 

  65. 

  66. def temperature(CHARLS_data):
    67. 

  67.     years = [2011, 2013,2015, 2018, 2020]
    68. 

  68.     for year in years:
    69. 

  69.         #读取日照数据
    70. 

  70.         temperature_data_last = pd.read_excel(f"气象数据/2000-2020年按年逐年气温/【{year-1}年】逐年气温.xlsx")
    71. 

  71.         temperature_data_last = temperature_data_last[["城市", "平均气温"]]
    72. 

  72.         temperature_data_last = temperature_data_last.rename(columns={"平均气温":"last_temperature"})
    73. 

  73.         temperature_data_before_last = pd.read_excel(f"气象数据/2000-2020年按年逐年气温/【{year-2}年】逐年气温.xlsx")
    74. 

  74.         temperature_data_before_last = temperature_data_before_last[["城市", "平均气温"]]
    75. 

  75.         temperature_data_before_last = temperature_data_before_last.rename(columns={"平均气温":"before_temperature"})
    76. 

  76.         #开始筛选出year的数据
    77. 

  77.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    78. 

  78.         #合并日照
    79. 

  79.         table_merge = pd.merge(CHARLS_data_year, temperature_data_last, left_on="city", right_on="城市", how='left')
    80. 

  80.         table_merge = pd.merge(table_merge, temperature_data_before_last, left_on="city", right_on="城市", how='left')
    81. 

  81.         #更新CHARLS表
    82. 

  82.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_temperature'] = table_merge['last_temperature'].values
    83. 

  83.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_temperature'] = table_merge['before_temperature'].values
    84. 

  84.         CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    85. 

  85.         print(year)
    86. 

  86. 

  87. def humidity(CHARLS_data):
    88. 

  88.     years = [2011, 2013,2015, 2018, 2020]
    89. 

  89.     for year in years:
    90. 

  90.         #读取日照数据
    91. 

  91.         humidity_data_last = pd.read_excel(f"气象数据/2000-2020年按年逐年湿度/【{year-1}年】逐年湿度.xlsx")
    92. 

  92.         humidity_data_last = humidity_data_last[["城市", "平均湿度"]]
    93. 

  93.         humidity_data_last = humidity_data_last.rename(columns={"平均湿度":"last_humidity"})
    94. 

  94.         humidity_data_before_last = pd.read_excel(f"气象数据/2000-2020年按年逐年湿度/【{year-2}年】逐年湿度.xlsx")
    95. 

  95.         humidity_data_before_last = humidity_data_before_last[["城市", "平均湿度"]]
    96. 

  96.         humidity_data_before_last = humidity_data_before_last.rename(columns={"平均湿度":"before_humidity"})
    97. 

  97.         #开始筛选出year的数据
    98. 

  98.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    99. 

  99.         #合并日照
    100. 

  100.         table_merge = pd.merge(CHARLS_data_year, humidity_data_last, left_on="city", right_on="城市", how='left')
    101. 

  101.         table_merge = pd.merge(table_merge, humidity_data_before_last, left_on="city", right_on="城市", how='left')
    102. 

  102.         #更新CHARLS表
    103. 

  103.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_humidity'] = table_merge['last_humidity'].values
    104. 

  104.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_humidity'] = table_merge['before_humidity'].values
    105. 

  105.         CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    106. 

  106.         print(year)
    107. 

  107. 

  108. if __name__ == "__main__":
    109. 

  109.     #读取CHARLS数据
    110. 

  110.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants.csv")
    111. 

  111.     CHARLS_data.to_csv("CHARLS_data_pollutants_mete.csv",index=False)
    112. 

  112.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants_mete.csv")
    113. 

  113.     sunlight(CHARLS_data)
    114. 

  114.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants_mete.csv")
    115. 

  115.     wind(CHARLS_data)
    116. 

  116.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants_mete.csv")
    117. 

  117.     rain(CHARLS_data)
    118. 

  118.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants_mete.csv")
    119. 

  119.     temperature(CHARLS_data)
    120. 

  120.     CHARLS_data = pd.read_csv("CHARLS_data_pollutants_mete.csv")
    121. 

  121.     humidity(CHARLS_data)
    122. 

  122. 

  123. 

  124. 

  125.