#程序文件ex2_11_2.py import string, random, collections #依次加载三个模块 x=string.ascii_letters+string.digits y=''.join([random.choice(x) for i in range(1000)]) count=collections.Counter(y) for k,v in sorted(count.items()): print(k, ':', v) 代码语言:javascript 复制 0 : 28 ...
I went to see it with my wife and my daughters on Tuesday afternoon. Although I didn't expect it to be very entertaining, it turned out to be loads of fun. We would definitely go back and see it again given the chance. LSTM 试图做到这一点—在忘记所有不相关信息的同时记住句子中的相关...
str1 = 'I am a unicode string' type(str1) # type(str1) => 'str' str2 = b"And I can't be concatenated to a byte string" type(str2) # type(str2) => 'bytes' str3 = str1 + str2 --- Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError...
string.hexdigits则不仅包含了string.digits中的所有字符,还包含了'abcdefABCDEF',也就是十六进制数字要用到的字符。string.octdigits包含了'01234567',八进制数字只用到了这些数字。string.ascii_lowercase包含了所有小写ASCII字母字符,string.ascii_uppercase包含了所有大写ASCII字母字符,而string.ascii_letters则包含了st...
StringDtype Timedelta TimedeltaIndex TimestampUInt16Dtype UInt32Dtype UInt64Dtype UInt64Index UInt8Dtypeapi array arrays bdate_range compatconcat core crosstab cut date_rangedescribe_option errors eval factorize get_dummiesget_option infer_freq interval_range io isnaisnull json_normalize lreshape melt ...
df_pivoted=df.pivot(index='date',columns='variable',values='value') 使用Multi-Index进行stack和unstack:将具有多级列的DataFrame转换为更紧凑的形式。 stacked=df.stack()unstacked=stacked.unstack() 字符串和类别类型之间的转换:将数据类型转换为优化内存使用的格式。
import sysvariable = 30print(sys.getsizeof(variable)) # 24 4.字节大小计算以下方法将以字节为单位返回字符串长度。def byte_size(string):return(len(string.encode('utf-8')))byte_size('1234') # 4byte_size('Hello World') # 11 5.重复打印字符串 N 次以下代码不需要使用循环即可打印某个字符串...
(Stacked Histogram for Continuous Variable) 22、类别变量堆积直方图(Stacked Histogram for Categorical Variable) 23、密度图(Density Plot) 24、带直方图的密度图(Density Curves with Histogram) 25、山峰叠峦图(Joy Plot) 26、分布点图(Distributed Dot Plot) 27、箱图(boxplot) 28、箱图结合点图(Dot + ...
var_name:变量列名称,如果为None则为variable value_name:默认为value 一、分组 1.groupby obj.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) # 返回一个pandas分组可迭代对象,包含(name,group)两个要素,可以是否for循环迭代输出 by:fuction...
outputs = tf.concat((fw_outputs, bw_outputs), 2) return outputs def AttentionLayer(self, inputs, name): #inputs是GRU的输出,size是[batch_size, max_time, encoder_size(hidden_size * 2)] with tf.variable_scope(name): # u_context是上下文的重要性向量,用于区分不同单词/句子对于句子/文档的...