上节课我们讲述了如何查看数据。
本节课我们来讲述如何选择数据。
# 1.Series
# 1.1 索引
Series 对象索引的工作原理和 ndarray 对象索引非常类似，不同的一点是，在对 Series 对象进行索引时，我们不但可以使用整数还可以使用 Series 对象本身的索引，举几个例子。 单个元素：
import pandas as pd
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series[3])
print(my_series['d'])
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在上面的代码中，my_series[3] 和 my_series['d'] 访问的是同一个元素，一个使用的是整数 3，一个使用的是 Series 对象本身的索引 d。
多个元素：
import pandas as pd
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series[[1, 3, 4]])
print(my_series[['b', 'd', 'e']])
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切片：
import pandas as pd
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series[0:5])
print(my_series['a':'e'])
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使用整数进行切片和使用 Series 对象的索引进行切片不同的地方是，使用整数切片不包含最末的那个元素，而使用对象的索引进行切片时是包含最末的那个元素的。 布尔索引：
import pandas as pd
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series[my_series > my_series.median()])
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上面代码的输出结果是大于平均值的元素。
# 1.2 像字典一样选择数据
import pandas as pd
import numpy as np
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series['b'])
print(my_series.get('b', np.NaN))
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判断 key 是否在 Series 里：
import pandas as pd
import numpy as np
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print('e' in my_series)
print('g' in my_series)
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# 1.3 loc 或 iloc
loc 和 iloc 的区别是，loc 使用的是轴标签，iloc 使用的是整数。
单个元素：
import pandas as pd
import numpy as np
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series.iloc[1])
print(my_series.loc['b'])
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多个元素：
import pandas as pd
import numpy as np
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series.iloc[[1, 3]])
print(my_series.loc[['b', 'd']])
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切片：
import pandas as pd
import numpy as np
my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])
print(my_series.iloc[1:5])
print(my_series.loc['b': 'e'])
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使用整数进行切片和使用 Series 对象的索引进行切片不同的地方是，使用整数切片不包含最末的那个元素，而使用对象的索引进行切片时是包含最末的那个元素的。
# 2.DataFrame
# 2.1 索引
使用索引的方法来访问 DataFrame 时，得到的是一列或多列。 单列的访问：
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df['Open'])
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上面的单列访问我们还可以使用另外一种方法，例如：
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df.Open)
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上面代码采用的方法是类似于属性的访问方法。 多列的访问：
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df[['Open', 'Close']])
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布尔索引：
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df[df['Open'] > 140])
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上面代码中，Open 列的值大于 140 的为后面三行，所以最后得到整个 DataFrame 的后面三行。使用布尔索引，我们还可以得到整个 DataFrame 中大于 140 的元素，例如：
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df[df > 140])
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上面的代码得到了整个 DataFrame 中值大于 140 的元素，不大于 140 的元素用 NaN 填充。
# 2.2 切片
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df[:3])
print(df[::2])
print(df[::-1])
print(df[1:2])
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上面的切片都是针对行来的。
# 2.3 loc 或 iloc
# 2.3.1 单行或单列的访问
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df.iloc[0])
print(df.loc['2021-07-01'])
print(df.iloc[:, 0])
print(df.loc[:, 'Open'])
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# 2.3.2 多行或多列的访问
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df.iloc[[0, 1, 2]])
print(df.iloc[:, [0, 1, 2]])
print(df.loc[:, ['Open', 'High', 'Low']])
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# 2.3.3 位于行列交叉的元素
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df.loc[['2021-07-01', '2021-07-02'], ['Open', 'High']])
print(df.iloc[[0, 1], [0, 1]])
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# 切片
import pandas as pd
d = {
"Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
"Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}
df = pd.DataFrame(d)
print(df.loc['2021-07-01':'2021-07-07', 'Open': 'Low'])
print(df.iloc[0:4, 0:3])
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# 3.总结
本节课我们讲述了如何提取 Series 对象或 DataFrame 对象的数据。
# 4.练习题
创建一个 DataFrame，并分别通过 loc 和 iloc 来访问其中的元素。