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pandas系列1_对象创建及查看数据

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本文重点知识：

  • 创建带有日期的索引：dates = pd.date_range('20190924', periods=6)
  • head()、tail()
  • 按轴排序：索引排序sort_index，默认是ascending=True升序
    • axis=0：行索引，可以用index
    • axis=1：列索引，可以用columns
  • 按值排序：df.sort_values(by='columns')，默认升序

创建数据

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import numpy as np
import pandas as pd
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s = pd.Series([1, 3, 5, np.nan, 6, 89])
s
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0     1.0
1     3.0
2     5.0
3     NaN
4     6.0
5    89.0
dtype: float64
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dates = pd.date_range('20190924', periods=6)
dates
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DatetimeIndex(['2019-09-24', '2019-09-25', '2019-09-26', '2019-09-27',
               '2019-09-28', '2019-09-29'],
              dtype='datetime64[ns]', freq='D')
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df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list("ABCD"))
df
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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# 同时创建多个不同的列
df2 = pd.DataFrame({'A': 1.,   #  某列的值相同
                    'B': pd.Timestamp('20130102'),  # 时间戳的创建
                    'C': pd.Series(1, index=list(range(4)), dtype='float32'),  # 某列值可以是S型数据
                    'D': np.array([3] * 4, dtype='int32'),  # 使用numpy数组
                    'E': pd.Categorical(["test", "train", "test", "train"]), # 不同的类
                    'F': 'foo'})  # 使用布尔值

df2
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo 
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df2.dtypes
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A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

查看数据

查看数据的相关信息

  • 头、尾几行数据
  • index、columns
  • describe ,T
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# 前几行数据，默认是5行
df.head(3)
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101 
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# 查看末几行，默认是5行
df.tail(2)
A B C D
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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# 查看数据的行索引
df.index
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DatetimeIndex(['2019-09-24', '2019-09-25', '2019-09-26', '2019-09-27',
               '2019-09-28', '2019-09-29'],
              dtype='datetime64[ns]', freq='D')
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# 查看数据的列属性
df.columns
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Index(['A', 'B', 'C', 'D'], dtype='object')
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df.to_numpy()
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array([[ 0.50000505,  0.16657823, -0.75851253, -0.67917279],
       [ 0.09020909,  0.11790626, -0.40218323,  2.26118249],
       [-0.80905211, -0.17314443,  0.32491161, -1.88510066],
       [ 0.31037417, -1.50208882,  1.52440064,  0.26995285],
       [-0.8464884 , -0.23587797,  1.39889574, -0.22957318],
       [ 0.9758533 , -0.99839501, -0.51448041, -0.88270375]])
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df.describe()
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.036817 -0.437504 0.262172 -0.190903
std 0.730717 0.668113 0.997562 1.400993
min -0.846488 -1.502089 -0.758513 -1.885101
25% -0.584237 -0.807766 -0.486406 -0.831821
50% 0.200292 -0.204511 -0.038636 -0.454373
75% 0.452597 0.045144 1.130400 0.145071
max 0.975853 0.166578 1.524401 2.261182 
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df.T
2019-09-24 00:00:00 2019-09-25 00:00:00 2019-09-26 00:00:00 2019-09-27 00:00:00 2019-09-28 00:00:00 2019-09-29 00:00:00
A 0.500005 0.090209 -0.809052 0.310374 -0.846488 0.975853
B 0.166578 0.117906 -0.173144 -1.502089 -0.235878 -0.998395
C -0.758513 -0.402183 0.324912 1.524401 1.398896 -0.514480
D -0.679173 2.261182 -1.885101 0.269953 -0.229573 -0.882704 
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# sort_index
# 索引排序`sort_index`，默认是`ascending=True`升序
# axis=0：行索引，可以用`index`
# axis=1：列索引，可以用`columns`
df.sort_index(axis=1)
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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# 按照行索引进行降序
df.sort_index(axis=0, ascending=False)
A B C D
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-24 0.500005 0.166578 -0.758513 -0.679173 
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# 按照某个属性进行排序
# 按照B属性的升序排列
df.sort_values(by="B")
A B C D
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-24 0.500005 0.166578 -0.758513 -0.679173 
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df.sort_values(by=["B","C"])
A B C D
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-24 0.500005 0.166578 -0.758513 -0.679173

选择数据

查看指定的行列数据

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# 指定列属性查看数据
df[["B","C"]]
B C
2019-09-24 0.166578 -0.758513
2019-09-25 0.117906 -0.402183
2019-09-26 -0.173144 0.324912
2019-09-27 -1.502089 1.524401
2019-09-28 -0.235878 1.398896
2019-09-29 -0.998395 -0.514480 
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# 指定行标签查看指定的行数据
df[1:3]
A B C D
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101 
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df["20190924":"20190927"]
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-27 0.310374 -1.502089 1.524401 0.269953

loc

根据标签(不是自带的数字索引)查看数据

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df.loc[dates[0]]
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A    0.500005
B    0.166578
C   -0.758513
D   -0.679173
Name: 2019-09-24 00:00:00, dtype: float64
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dates[0]
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Timestamp('2019-09-24 00:00:00', freq='D')
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# 选择行和列
df.loc[:, ["A","B"]]   # 选择所有行和AB两个列
A B
2019-09-24 0.500005 0.166578
2019-09-25 0.090209 0.117906
2019-09-26 -0.809052 -0.173144
2019-09-27 0.310374 -1.502089
2019-09-28 -0.846488 -0.235878
2019-09-29 0.975853 -0.998395 
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# 索引通过标签来实现
df.loc['20190924':'20190927', ['A', 'B']]
A B
2019-09-24 0.500005 0.166578
2019-09-25 0.090209 0.117906
2019-09-26 -0.809052 -0.173144
2019-09-27 0.310374 -1.502089 
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# 指定的行或者列可以是切片形式
df.loc['20190924':'20190927', 'A':'B']
A B
2019-09-24 0.500005 0.166578
2019-09-25 0.090209 0.117906
2019-09-26 -0.809052 -0.173144
2019-09-27 0.310374 -1.502089

iloc数字索引

记忆放法：iloc记为intloc，int为整型，表示通过数字来进行索引

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df
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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df.iloc[1:3]
A B C D
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101 
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df.iloc[1:3, 0:2]  # 切片形式，连续性
A B
2019-09-25 0.090209 0.117906
2019-09-26 -0.809052 -0.173144 
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df.iloc[[1, 2, 4], [0, 2]]  # 行索引是离散的值
A C
2019-09-25 0.090209 -0.402183
2019-09-26 -0.809052 0.324912
2019-09-28 -0.846488 1.398896 
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df.iloc[:, 1:3]
B C
2019-09-24 0.166578 -0.758513
2019-09-25 0.117906 -0.402183
2019-09-26 -0.173144 0.324912
2019-09-27 -1.502089 1.524401
2019-09-28 -0.235878 1.398896
2019-09-29 -0.998395 -0.514480

获取具体位置的元素

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df.iloc[1,2]
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-0.4021832300071616
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df.iat[1,2]    # 等同上面
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-0.4021832300071616

布尔索引

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df
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-26 -0.809052 -0.173144 0.324912 -1.885101
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-28 -0.846488 -0.235878 1.398896 -0.229573
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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df[df.A > 0]  # 将属性A中大于0的行全部选择出出来
A B C D
2019-09-24 0.500005 0.166578 -0.758513 -0.679173
2019-09-25 0.090209 0.117906 -0.402183 2.261182
2019-09-27 0.310374 -1.502089 1.524401 0.269953
2019-09-29 0.975853 -0.998395 -0.514480 -0.882704 
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df[df > 0]
A B C D
2019-09-24 0.500005 0.166578 NaN NaN
2019-09-25 0.090209 0.117906 NaN 2.261182
2019-09-26 NaN NaN 0.324912 NaN
2019-09-27 0.310374 NaN 1.524401 0.269953
2019-09-28 NaN NaN 1.398896 NaN
2019-09-29 0.975853 NaN NaN NaN

本文标题:pandas系列1_对象创建及查看数据

发布时间:2019年09月26日 - 08:09

原始链接:http://www.renpeter.cn/2019/09/26/pandas%E7%B3%BB%E5%88%971-%E5%AF%B9%E8%B1%A1%E5%88%9B%E5%BB%BA%E4%B8%8E%E6%9F%A5%E7%9C%8B%E6%95%B0%E6%8D%AE.html

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