pandas编程技巧3

pandas编程技巧3

1. np.nan_to_num(x)

   使用0替代数组x中的nan,使用有限的数字代替inf元素

   使用背景：在是使用numpy数组时，常常会出现nan或者inf的元素，可能会造成数值计算的一些错误。可以使用numpy库的函数nan_to_num(),使得nan和inf能够最简单地转换成相应的数值。

   from pandas import DataFrame, Series
   import numpy as np
   
   a = np.array([
       [np.nan, np.inf, 0, 10],
       [np.inf, np.nan, np.nan, 3]
       ])
   print(a)
   print("nan_tp_num()转换后的结果:")
   print(np.nan_to_num(a))
   
   ----------------------------result----------------------
   [[nan inf  0. 10.]
    [inf nan nan  3.]]
   nan_tp_num()转换后的结果:
   [[0.00000000e+000 1.79769313e+308 0.00000000e+000 1.00000000e+001]
    [1.79769313e+308 0.00000000e+000 0.00000000e+000 3.00000000e+000]]
   

2. SparkSession.createDataFrame()

   可以获得从 rdd、 python list 和 pandas df 创建 df 的能力

   在 Pyspark 操纵 spark-SQL 的世界里借助 session 这个客户端来对内容进行操作和计算

   spark sql的相关链接请参考：https://www.cnblogs.com/piperck/p/10446720.html

   from pyspark.sql import Row
   from pyspark import SparkConf,SparkContext, SQLContext
   from pyspark.sql.types import StructType, StructField, StringType, DateType, IntegerType, FloatType
   
   
   def parse_line(line):
       ll = line.strip().split(",")
       return ll[0], ll[1]
   
   if __name__ == "__main__":
       conf = SparkConf()
       conf.setAppName("test createDataFrame function...")
       sc = SparkContext(conf=conf)
       spark = SQLContext(sc)
       data_rdd = sc.textFile("/user/xxxx/test/test.txt").map(parse_line)
       #print(data_rdd)
       schema = StructType([StructField("id", StringType(), True),
           StructField("user_name", StringType(), True)
           ])
       data_df = spark.createDataFrame(data_rdd,schema=schema)
       #data_df.printSchema
       #data_df.show(false)
       print("*"*30, data_df)
       print("*"*30, data_df.collect())
       data_df_pandas = data_df.toPandas()  #将spark的DataFrame转化为pandas的dataFrame
       print("-"*30, data_df_pandas)
       
       -------------------------------result------------------
       ('******************************', DataFrame[id: string, user_name: string])
       ('******************************', [Row(id=u'aa', user_name=u'11'), Row(id=u'bb', user_name=u'22'), Row(id=u'cc', user_name=u'33'), Row(id=u'dd', user_name=u'44')])
       ------------------------------    id user_name
   0  aa        11
   1  bb        22
   2  cc        33
   3  dd        44
       
       
   附件：    
   text.txt的内容：
   aa,11
   bb,22
   cc,33
   dd,44
   

   3. loc和iloc的区别

      loc():只能用index行标和columns列标来筛选数据，即不能出现数字下标索引

      iloc():只能用数字下标索引来筛选字段，即不能出现字符串

      from pandas import DataFrame, Series
      
      from pandas import DataFrame, Series
      
      df = DataFrame([
          [1,2,3,4],
          [5,6,7,8],
          [9,10,11,12]
          ])
      
      df.columns = list("abcd")
      df.index = list("123")
      print(df)
      print("*"*10)
      print(df.loc["1":"2","a"])
      print("*"*10)
      print(df["a"])
      print("*"*10)
      print(df.iloc[:2, :2])
      print("*"*30)
      print(df["a"][:3])  #先筛选第"a"列，再筛选其前3行
      print(df[:3][["a","b"]]) #先筛选前3行想，再筛选其"a"和"b"列
      
      ----------------------------result-------------------
         a   b   c   d
      1  1   2   3   4
      2  5   6   7   8
      3  9  10  11  12
      **********
      1    1
      2    5
      Name: a, dtype: int64
      **********
      1    1
      2    5
      3    9
      Name: a, dtype: int64
      **********
         a  b
      1  1  2
      2  5  6
      ******************************
      1    1
      2    5
      3    9
      Name: a, dtype: int64
         a   b
      1  1   2
      2  5   6
      3  9  10
      

   4. rename()

      from pandas import DataFrame, Series
      
      df = DataFrame([
          [1,2,3,4],
          [5,6,7,8],
          [9,10,11,12]
          ])
      
      df.columns = list("abcd")
      df.index = list("123")
      print(df)
      print("*"*10)
      df.rename({"a":"aa"}, axis = 1, inplace=True)
      print(df)
      
      ------------------------result------------------
         a   b   c   d
      1  1   2   3   4
      2  5   6   7   8
      3  9  10  11  12
      **********
         aa   b   c   d
      1   1   2   3   4
      2   5   6   7   8
      3   9  10  11  12
      

   5. apply()

      from pandas import DataFrame, Series
      
      info = [
          {"order_id":1001, "name":"wangwu", "score_1":90, "score_2":85},
          {"order_id":1002, "name":"zhangsan", "score_1":60, "score_2":75},
          {"order_id":1003, "name":"lisi", "score_1":96, "score_2":95},
          {"order_id":1001, "name":"wang wu", "score_1":89, "score_2":90}
          ]
      
      df = DataFrame(info)
      df["score1_level"] = df.apply(lambda x: 1 if x.score_1 > 90 else (0 if x.score_1 > 60 else -1), axis = 1)
      df["score2_level"] = df.apply(lambda x: 1 if x.score_2 > 90 else (0 if x.score_2 > 60 else -1), axis = 1)
      df["score_total"] = df.apply(lambda x: x.score_1 + x.score_2, axis = 1)
      print(df)
      
      --------------------------result---------------------
         order_id      name  score_1  score_2  score1_level  score2_level  score_total
      0      1001    wangwu       90       85             0             0          175
      1      1002  zhangsan       60       75            -1             0          135
      2      1003      lisi       96       95             1             1          191
      3      1001   wang wu       89       90             0             0          179
      

来源：CSDN

作者：追梦杏花天影

链接：https://blog.csdn.net/u010569893/article/details/103683396