# 测试数据test_values=[0.00345,1000,123.4500,0.00456,5000.0]# 遍历测试数据并输出有效数字的位数forvalueintest_values:print(f"{value}的有效数字位数是:{count_significant_figures(value)}") 1. 2. 3. 4. 5. 6. 代码解释: 我们定义了一个包含多个测试值的列表test_values。 使用for循环遍历每个值,并...
我们将定义一个函数count_significant_figures()以计算有效数字。 defcount_significant_figures(num_str):# 移除空格和前导零num_str=num_str.strip().lstrip('0')# 如果数字是空字符串,返回0ifnum_str==''ornum_str=='.':return0# 一旦找到小数点,之后的零都是有效数字if'.'innum_str:integer_part,d...
(math.log10(abs(num))) + 1 # 计算需要保留的小数位数 decimal_places = sig_figs - digits # 进行四舍五入 rounded_num = round(num, decimal_places) return rounded_num # 示例 print(round_to_significant_figures(123.456)) # 输出: 123.0 print(round_to_significant_figures(0.0012345)) # 输出:...
The default precision is six significant figures. Let’s take the following complex number as an example and format it with two decimal places on both parts: Python >>> z = pow(3 + 2j, 0.5) >>> print(z) (1.8173540210239707+0.5502505227003375j) A quick way to do this is either by...
The indentation on the line after a \ line continuation is not significant. For example, the following is valid Python code: print('Four score and seven ' + \ 'years ago...') These tricks are useful when you want to rearrange long lines of Python code to be a bit more readable. ...
The decimal point “floats” around to accommodate a varying number of significant figures, except it’s a binary point. Two data types conforming to that standard are widely supported: Single precision: 1 sign bit, 8 exponent bits, 23 mantissa bits Double precision: 1 sign bit, 11 exponent...
Several years ago, a new version of Python (3) was introduced. This new version has some small but significant changes from the previous one. The most visible change for beginners is thatprintwhich used to be a Python keyword >>> print "Hello World!" # for Python 2 ...
plt.figure(figsize = (16,6)) # Create matplotlib figure sns.heatmap(df.corr(), annot = True, linewidths=1, fmt=".2g", cmap= 'coolwarm') # fmt = .1e (scientific notation), .2f (2 decimal places), .3g(3 significant figures), .2%(percentage with 2 decimal places) plt.xticks(...
print(rounded_prices) Output: [124. 234. 346.] After executing the code in Pycharm, one can see the output in the below screenshot. 4. NumPy round to significant figures Let’s create a custom Python function with the np.round() to round a specific number to a defined number of sign...
# 测试我们的有效位数函数test_numbers=[0.00456,123.45,1000,0.004560,123.4500,1.23e10,456e-5]fornumberintest_numbers:ef=effective_significant_figures(number)print(f"数字{number}的有效位数为:{ef}") 1. 2. 3. 4. 5. 6. 第四部分:结果输出 ...