121 lines
3.3 KiB
Markdown
121 lines
3.3 KiB
Markdown
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# Python 中频率表的介绍
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> 原文：<https://www.askpython.com/python/examples/frequency-tables>
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描述几个类别出现频率的表格称为**频率表**。这种特殊的表特别有助于了解数据集中包含的值的分布情况。本教程将带您完成用 Python 创建频率表的过程。我们将在接下来的几节中以不同的方式介绍相同的实现。
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***也读作:[计算熊猫数据帧行数的 6 种方法](https://www.askpython.com/python-modules/pandas/count-pandas-dataframe-rows)***
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* * *
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## 方法 1–借助 value_counts()函数
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第一种方法是利用`value_counts()`函数，该函数将返回一个包含所有值列表中唯一值的计数的序列。结果将按照`descending`的顺序排列，这意味着第一个元素是最频繁出现的元素。
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```py
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import pandas as pd
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data = pd.Series([1, 2, 5, 2, 3, 3, 3, 3, 4, 4, 5])
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print("The Dataset is : ")
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print(data)
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print("\nFrequency Table for the data : ")
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print(data.value_counts())
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```
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```py
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The Dataset is :
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0 1
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1 2
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2 5
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3 2
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4 3
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5 3
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6 3
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7 3
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8 4
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9 4
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10 5
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dtype: int64
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Frequency Table for the data :
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3 4
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2 2
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5 2
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4 2
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1 1
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```
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* * *
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## 方法 2–借助**交叉表**()函数
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我们可以用来显示熊猫数据帧频率的另一个函数是 **`crosstab()`** 函数，如下面的代码所示。我们将创建一个数据帧，然后为数据帧的两列创建频率表。
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```py
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df = pd.DataFrame({'Student_Grade': ['A','B','A','B','B', 'B', 'B', 'C', 'C', 'D'],
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'Student_Age': [18, 25, 28, 19, 30, 20, 15, 18, 29, 17],
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'Student_Gender': ['M','F', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'F']})
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print("The Dataset is : ")
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print(df)
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print("\nFrequency Table for the Grade in the dataset : ")
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pd.crosstab(index=df['Student_Grade'], columns='count')
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```
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Crosstab Output Screenshot
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```py
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print("\nFrequency Table for the Gender in the dataset : ")
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pd.crosstab(index=df['Student_Gender'], columns='count')
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```
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Crosstab Output Screenshot 2
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* * *
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## 高级频率表(双向表)
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我们还可以创建一个**双向频率表**来显示我们在上一节中使用的数据集中两个不同列的频率。下面的代码显示了两列 Age 和 Grade 的双向频率表。
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```py
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pd.crosstab(index=df['Student_Grade'], columns=df['Student_Age'])
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```
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Two Way Freq Table Output 1
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我们还将在性别和年级两栏之间开发一个双向频率表。看看下面的代码。
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```py
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pd.crosstab(index=df['Student_Grade'], columns=df['Student_Gender'])
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```
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Two Way Freq Table Output 2
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* * *
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感谢您的阅读！我希望你理解了教程😃
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我建议您也阅读以下教程:
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1. [Python 中的计算精度—分类误差度量](https://www.askpython.com/python/examples/calculating-precision)
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2. [Python 中的卡方检验——您需要知道的一切！！](https://www.askpython.com/python/examples/chi-square-test)
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3. [泛 NumPy 三角函数认识](https://www.askpython.com/python/numpy-trigonometric-functions)
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* * *
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