pandas入门学习第一天第一部分_pandas第一天作业-优快云博客

本文链接：https://blog.youkuaiyun.com/sara686/article/details/103102992
本文通过实例演示了使用Pandas库进行数据操作的过程，包括数据导入、数据索引、数据排序、数据切片、数据选择等关键操作。通过具体代码展示了如何创建DataFrame，如何使用loc和iloc进行数据定位，以及如何更新数据。
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In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: s = pd.Series([1,3,5,np.Na])
In [4]: dates = pd.date_range("20160301", periods=6)
In [5]: dates
DatetimeIndex(['2016-03-01', '2016-03-02', '2016-03-03', '2016-03-04',
               '2016-03-05', '2016-03-06'],
              dtype='datetime64[ns]', freq='D')
  In [6]: data = pd.DataFrame(np.random.randn(6,4), index=dates,columns=list("ABCD"))
  In [7]: data
  Out[7]:
                   A         B         C         D
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
2016-03-05  0.850521 -0.302853 -0.978198 -0.991488
2016-03-06  1.505182 -0.087285  1.641413  2.316626
In [8]: data.shape
Out[8]: (6, 4)
In [9]: data.values
Out[9]:
array([[-0.22208318, -0.96338087, -0.17209986,  0.69057764],
       [ 0.01076758, -0.14935166, -0.40825718,  1.34124888],
       [ 1.80867181, -0.23454168, -0.56645639,  0.58882996],
       [-0.31567662, -1.33944465,  1.15407395,  0.41100917],
       [ 0.85052137, -0.30285326, -0.97819807, -0.99148776],
       [ 1.50518199, -0.08728479,  1.64141316,  2.31662617]])
 In [11]: data.index
 Out[11]:
DatetimeIndex(['2016-03-01', '2016-03-02', '2016-03-03', '2016-03-04',
               '2016-03-05', '2016-03-06'],
              dtype='datetime64[ns]', freq='D')
  In [12]: data.columns
  Out[12]: Index(['A', 'B', 'C', 'D'], dtype='object')
In [15]: data.describe()
Out[15]:
              A         B         C         D
count  6.000000  6.000000  6.000000  6.000000
mean   0.606230 -0.512809  0.111746  0.726134
std    0.916979  0.514009  1.041656  1.093405
min   -0.315677 -1.339445 -0.978198 -0.991488
25%   -0.163870 -0.798249 -0.526907  0.455464
50%    0.430644 -0.268697 -0.290179  0.639704
75%    1.341517 -0.170649  0.822531  1.178581
max    1.808672 -0.087285  1.641413  2.316626
In [17]: data.T
Out[17]:
   2016-03-01  2016-03-02  2016-03-03  2016-03-04  2016-03-05  2016-03-06
A   -0.222083    0.010768    1.808672   -0.315677    0.850521    1.505182
B   -0.963381   -0.149352   -0.234542   -1.339445   -0.302853   -0.087285
C   -0.172100   -0.408257   -0.566456    1.154074   -0.978198    1.641413
D    0.690578    1.341249    0.588830    0.411009   -0.991488    2.316626
In [18]: data
Out[18]:
                   A         B         C         D
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
2016-03-05  0.850521 -0.302853 -0.978198 -0.991488
2016-03-06  1.505182 -0.087285  1.641413  2.316626
In [19]: data.sort_index(axis=1)
Out[19]:
                   A         B         C         D
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
2016-03-05  0.850521 -0.302853 -0.978198 -0.991488
2016-03-06  1.505182 -0.087285  1.641413  2.316626
In [21]: data.sort_index(axis=1,ascending=False)
Out[21]:
                   D         C         B         A
2016-03-01  0.690578 -0.172100 -0.963381 -0.222083
2016-03-02  1.341249 -0.408257 -0.149352  0.010768
2016-03-03  0.588830 -0.566456 -0.234542  1.808672
2016-03-04  0.411009  1.154074 -1.339445 -0.315677
2016-03-05 -0.991488 -0.978198 -0.302853  0.850521
2016-03-06  2.316626  1.641413 -0.087285  1.505182
In [22]: data.sort_values(by="A")
Out[22]:
                   A         B         C         D
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-05  0.850521 -0.302853 -0.978198 -0.991488
2016-03-06  1.505182 -0.087285  1.641413  2.316626
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
In [23]: data["A"]
Out[23]:
2016-03-01   -0.222083
2016-03-02    0.010768
2016-03-03    1.808672
2016-03-04   -0.315677
2016-03-05    0.850521
2016-03-06    1.505182
In [24]: data[2:4]
Out[24]:
                   A         B         C         D
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
In [25]: data.loc["2016-03-01":"2016-03-04"]
Out[25]:
                   A         B         C         D
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
In [26]: data.iloc[2:4]
Out[26]:
                   A         B         C         D
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
In [27]: data.loc[:,["B","D"]]
Out[27]:
                   B         D
2016-03-01 -0.963381  0.690578
2016-03-02 -0.149352  1.341249
2016-03-03 -0.234542  0.588830
2016-03-04 -1.339445  0.411009
2016-03-05 -0.302853 -0.991488
2016-03-06 -0.087285  2.316626
In [28]: data.loc["2016-03-02":"2016-03-04",["B","D"]]
Out[28]:
                   B         D
2016-03-02 -0.149352  1.341249
2016-03-03 -0.234542  0.588830
2016-03-04 -1.339445  0.411009
In [30]: data.at[pd.Timestamp("2016-03-02"),"B"]
Out[30]: -0.14935166221886448
In [31]: data.iloc[1:3,2:4]
Out[31]:
                   C         D
2016-03-02 -0.408257  1.341249
2016-03-03 -0.566456  0.588830
In [32]: data.iloc[1,1]
Out[32]: -0.14935166221886448
In [33]: data.iat[1,1]
Out[33]: -0.14935166221886448
In [34]: data
Out[34]:
                   A         B         C         D
2016-03-01 -0.222083 -0.963381 -0.172100  0.690578
2016-03-02  0.010768 -0.149352 -0.408257  1.341249
2016-03-03  1.808672 -0.234542 -0.566456  0.588830
2016-03-04 -0.315677 -1.339445  1.154074  0.411009
2016-03-05  0.850521 -0.302853 -0.978198 -0.991488
2016-03-06  1.505182 -0.087285  1.641413  2.316626
  In [35]: data.iat[0,0]=100
  In [36]: data
Out[36]:
                     A         B         C         D
2016-03-01  100.000000 -0.963381 -0.172100  0.690578
2016-03-02    0.010768 -0.149352 -0.408257  1.341249
2016-03-03    1.808672 -0.234542 -0.566456  0.588830
2016-03-04   -0.315677 -1.339445  1.154074  0.411009
2016-03-05    0.850521 -0.302853 -0.978198 -0.991488
2016-03-06    1.505182 -0.087285  1.641413  2.316626
In [37]: data.A=range(6)
In [38]: data
Out[38]:
            A         B         C         D
2016-03-01  0 -0.963381 -0.172100  0.690578
2016-03-02  1 -0.149352 -0.408257  1.341249
2016-03-03  2 -0.234542 -0.566456  0.588830
2016-03-04  3 -1.339445  1.154074  0.411009
2016-03-05  4 -0.302853 -0.978198 -0.991488
2016-03-06  5 -0.087285  1.641413  2.316626
In [39]: data.B=200
In [40]: data
Out[40]:
            A    B         C         D
2016-03-01  0  200 -0.172100  0.690578
2016-03-02  1  200 -0.408257  1.341249
2016-03-03  2  200 -0.566456  0.588830
2016-03-04  3  200  1.154074  0.411009
2016-03-05  4  200 -0.978198 -0.991488
2016-03-06  5  200  1.641413  2.316626