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CHARLS_meteorology.py

CHARLS_meteorology.py 10 KB
Cronologia Originale

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

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

  9.         sunlight_data_cur = sunlight_data_cur.rename(columns={"累积日照":"cur_sunlight"})
    10. 

  10.         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")
    11. 

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

  12.         sunlight_data_last = sunlight_data_last.rename(columns={"累积日照":"last_sunlight"})
    13. 

  13.         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")
    14. 

  14.         sunlight_data_before_last = sunlight_data_before_last[["城市", "累积日照"]]
    15. 

  15.         sunlight_data_before_last = sunlight_data_before_last.rename(columns={"累积日照":"before_sunlight"})
    16. 

  16.         #开始筛选出year的数据
    17. 

  17.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    18. 

  18.         #合并日照
    19. 

  19.         table_merge = pd.merge(CHARLS_data_year, sunlight_data_cur, left_on="city", right_on="城市", how='left')
    20. 

  20.         table_merge = pd.merge(table_merge, sunlight_data_last, left_on="city", right_on="城市", how='left')
    21. 

  21.         table_merge = pd.merge(table_merge, sunlight_data_before_last, left_on="city", right_on="城市", how='left')
    22. 

  22.         #更新CHARLS表
    23. 

  23.         CHARLS_data.loc[CHARLS_data['wave']==year, 'cur_year_sunlight'] = table_merge['cur_sunlight'].values
    24. 

  24.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_sunlight'] = table_merge['last_sunlight'].values
    25. 

  25.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_sunlight'] = table_merge['before_sunlight'].values
    26. 

  26.         CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    27. 

  27.         print(year)
    28. 

  28. 

  29. def wind(CHARLS_data):
    30. 

  30.     years = [2011, 2013,2015, 2018, 2020]
    31. 

  31.     for year in years:
    32. 

  32.         #读取日照数据
    33. 

  33.         wind_data_cur = pd.read_excel(f"气象数据/2000-2020年按年逐年风速/【{year}年】逐年风速.xlsx")
    34. 

  34.         wind_data_cur = wind_data_cur[["城市", "平均风速"]]
    35. 

  35.         wind_data_cur = wind_data_cur.rename(columns={"平均风速":"cur_wind"})
    36. 

  36.         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")
    37. 

  37.         wind_data_last = wind_data_last[["城市", "平均风速"]]
    38. 

  38.         wind_data_last = wind_data_last.rename(columns={"平均风速":"last_wind"})
    39. 

  39.         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")
    40. 

  40.         wind_data_before_last = wind_data_before_last[["城市", "平均风速"]]
    41. 

  41.         wind_data_before_last = wind_data_before_last.rename(columns={"平均风速":"before_wind"})
    42. 

  42.         #开始筛选出year的数据
    43. 

  43.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    44. 

  44.         #合并日照
    45. 

  45.         table_merge = pd.merge(CHARLS_data_year, wind_data_cur, left_on="city", right_on="城市", how='left')
    46. 

  46.         table_merge = pd.merge(table_merge, wind_data_last, left_on="city", right_on="城市", how='left')
    47. 

  47.         table_merge = pd.merge(table_merge, wind_data_before_last, left_on="city", right_on="城市", how='left')
    48. 

  48.         #更新CHARLS表
    49. 

  49.         CHARLS_data.loc[CHARLS_data['wave']==year, 'cur_year_wind'] = table_merge['cur_wind'].values
    50. 

  50.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_wind'] = table_merge['last_wind'].values
    51. 

  51.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_wind'] = table_merge['before_wind'].values
    52. 

  52.         CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    53. 

  53.         print(year)
    54. 

  54. 

  55. def rain(CHARLS_data):
    56. 

  56.     years = [2011, 2013,2015, 2018, 2020]
    57. 

  57.     for year in years:
    58. 

  58.         #读取日照数据
    59. 

  59.         rain_data_cur = pd.read_excel(f"气象数据/2000-2020年按年逐年降水/【{year}年】逐年降水.xlsx")
    60. 

  60.         rain_data_cur = rain_data_cur[["城市", "累积降水"]]
    61. 

  61.         rain_data_cur = rain_data_cur.rename(columns={"累积降水":"cur_rain"})
    62. 

  62.         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")
    63. 

  63.         rain_data_last = rain_data_last[["城市", "累积降水"]]
    64. 

  64.         rain_data_last = rain_data_last.rename(columns={"累积降水":"last_rain"})
    65. 

  65.         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")
    66. 

  66.         rain_data_before_last = rain_data_before_last[["城市", "累积降水"]]
    67. 

  67.         rain_data_before_last = rain_data_before_last.rename(columns={"累积降水":"before_rain"})
    68. 

  68.         #开始筛选出year的数据
    69. 

  69.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    70. 

  70.         #合并日照
    71. 

  71.         table_merge = pd.merge(CHARLS_data_year, rain_data_cur, left_on="city", right_on="城市", how='left')
    72. 

  72.         table_merge = pd.merge(table_merge, rain_data_last, left_on="city", right_on="城市", how='left')
    73. 

  73.         table_merge = pd.merge(table_merge, rain_data_before_last, left_on="city", right_on="城市", how='left')
    74. 

  74.         #更新CHARLS表
    75. 

  75.         CHARLS_data.loc[CHARLS_data['wave']==year, 'cur_year_rain'] = table_merge['cur_rain'].values
    76. 

  76.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_rain'] = table_merge['last_rain'].values
    77. 

  77.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_rain'] = table_merge['before_rain'].values
    78. 

  78.         CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    79. 

  79.         print(year)
    80. 

  80. 

  81. def temperature(CHARLS_data):
    82. 

  82.     years = [2011, 2013,2015, 2018, 2020]
    83. 

  83.     for year in years:
    84. 

  84.         #读取日照数据
    85. 

  85.         temperature_data_cur = pd.read_excel(f"气象数据/2000-2020年按年逐年气温/【{year}年】逐年气温.xlsx")
    86. 

  86.         temperature_data_cur = temperature_data_cur[["城市", "平均气温"]]
    87. 

  87.         temperature_data_cur = temperature_data_cur.rename(columns={"平均气温":"cur_temperature"})
    88. 

  88.         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")
    89. 

  89.         temperature_data_last = temperature_data_last[["城市", "平均气温"]]
    90. 

  90.         temperature_data_last = temperature_data_last.rename(columns={"平均气温":"last_temperature"})
    91. 

  91.         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")
    92. 

  92.         temperature_data_before_last = temperature_data_before_last[["城市", "平均气温"]]
    93. 

  93.         temperature_data_before_last = temperature_data_before_last.rename(columns={"平均气温":"before_temperature"})
    94. 

  94.         #开始筛选出year的数据
    95. 

  95.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    96. 

  96.         #合并日照
    97. 

  97.         table_merge = pd.merge(CHARLS_data_year, temperature_data_cur, left_on="city", right_on="城市", how='left')
    98. 

  98.         table_merge = pd.merge(table_merge, temperature_data_last, left_on="city", right_on="城市", how='left')
    99. 

  99.         table_merge = pd.merge(table_merge, temperature_data_before_last, left_on="city", right_on="城市", how='left')
    100. 

  100.         #更新CHARLS表
    101. 

  101.         CHARLS_data.loc[CHARLS_data['wave']==year, 'cur_year_temperature'] = table_merge['cur_temperature'].values
    102. 

  102.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_temperature'] = table_merge['last_temperature'].values
    103. 

  103.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_temperature'] = table_merge['before_temperature'].values
    104. 

  104.         CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    105. 

  105.         print(year)
    106. 

  106. 

  107. def humidity(CHARLS_data):
    108. 

  108.     years = [2011, 2013,2015, 2018, 2020]
    109. 

  109.     for year in years:
    110. 

  110.         #读取日照数据
    111. 

  111.         humidity_data_cur = pd.read_excel(f"气象数据/2000-2020年按年逐年湿度/【{year}年】逐年湿度.xlsx")
    112. 

  112.         humidity_data_cur = humidity_data_cur[["城市", "平均湿度"]]
    113. 

  113.         humidity_data_cur = humidity_data_cur.rename(columns={"平均湿度":"cur_humidity"})
    114. 

  114.         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")
    115. 

  115.         humidity_data_last = humidity_data_last[["城市", "平均湿度"]]
    116. 

  116.         humidity_data_last = humidity_data_last.rename(columns={"平均湿度":"last_humidity"})
    117. 

  117.         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")
    118. 

  118.         humidity_data_before_last = humidity_data_before_last[["城市", "平均湿度"]]
    119. 

  119.         humidity_data_before_last = humidity_data_before_last.rename(columns={"平均湿度":"before_humidity"})
    120. 

  120.         #开始筛选出year的数据
    121. 

  121.         CHARLS_data_year = CHARLS_data[CHARLS_data['wave']==year]
    122. 

  122.         #合并日照
    123. 

  123.         table_merge = pd.merge(CHARLS_data_year, humidity_data_cur, left_on="city", right_on="城市", how='left')
    124. 

  124.         table_merge = pd.merge(table_merge, humidity_data_last, left_on="city", right_on="城市", how='left')
    125. 

  125.         table_merge = pd.merge(table_merge, humidity_data_before_last, left_on="city", right_on="城市", how='left')
    126. 

  126.         #更新CHARLS表
    127. 

  127.         CHARLS_data.loc[CHARLS_data['wave']==year, 'cur_year_humidity'] = table_merge['cur_humidity'].values
    128. 

  128.         CHARLS_data.loc[CHARLS_data['wave']==year, 'last_year_humidity'] = table_merge['last_humidity'].values
    129. 

  129.         CHARLS_data.loc[CHARLS_data['wave']==year, 'before_last_humidity'] = table_merge['before_humidity'].values
    130. 

  130.         CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    131. 

  131.         print(year)
    132. 

  132. 

  133. if __name__ == "__main__":
    134. 

  134.     #读取CHARLS数据
    135. 

  135.     CHARLS_data = pd.read_csv("CHARLS_data_p_n.csv")
    136. 

  136.     CHARLS_data.to_csv("CHARLS_data_p_n_m.csv",index=False)
    137. 

  137.     CHARLS_data = pd.read_csv("CHARLS_data_p_n_m.csv")
    138. 

  138.     sunlight(CHARLS_data)
    139. 

  139.     CHARLS_data = pd.read_csv("CHARLS_data_p_n_m.csv")
    140. 

  140.     wind(CHARLS_data)
    141. 

  141.     CHARLS_data = pd.read_csv("CHARLS_data_p_n_m.csv")
    142. 

  142.     rain(CHARLS_data)
    143. 

  143.     CHARLS_data = pd.read_csv("CHARLS_data_p_n_m.csv")
    144. 

  144.     temperature(CHARLS_data)
    145. 

  145.     CHARLS_data = pd.read_csv("CHARLS_data_p_n_m.csv")
    146. 

  146.     humidity(CHARLS_data)
    147. 

  147. 

  148. 

  149. 

  150.