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66 lines
1.4 KiB
66 lines
1.4 KiB
5 years ago
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# 13.1 初步分析数据
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这一步再熟悉不过了，可能会熟悉地让人心疼。但这又是数据挖掘非常重要也是必不可少的一步。
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```python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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datafr = pd.read_csv("./data.csv")
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# 查看前10行数据到底长什么样子
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datafr.head(10)
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```
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怎么样，是不是感觉有点懵，有这么多特征，还有一些缺失值。看来这个数据集并不是很好处理的样子。是的，我们需要一步一步脚印的来分析它。
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还是按照惯例，看一下数据集有多少行多少列。
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```python
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print("Dimension of the dataset is: ",datafr.shape)
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```
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总共 89 个特征！然后再看一下数据缺失的情况。
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```python
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# 统计出含有缺失值的特征的数量
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datafr.isnull().sum().sort_values(ascending=False)
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```
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总共 89 个特征，也就只有 13 个特征是完好无损的。不过值得庆幸的是，含有缺失值的特征们的缺失比例并不高，只有 Loaned From 这个特征的缺失严重。所以我们暂且可以认为这个特征没有什么用处，把它删掉就好了。
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```python
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# 删除Loaned From
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datafr.drop('Loaned From',axis=1,inplace=True)
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```
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