pandas

最新推荐文章于 2023-12-25 07:00:00 发布

csdn_lzw

最新推荐文章于 2023-12-25 07:00:00 发布

阅读量310

点赞数

分类专栏：利用python进行数据分析

本文链接：https://blog.youkuaiyun.com/csdn_lzw/article/details/79249547

版权

利用python进行数据分析专栏收录该内容

3 篇文章

订阅专栏

本文介绍了使用Python的Pandas库进行数据处理的方法，包括DataFrame的缺失值处理、索引重置、数据融合等操作，并展示了如何利用LabelEncoder进行类别编码，以及通过列表推导式简化常见数据操作。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

1.rest_index

当对DataFrame数据进行删除缺失值后，需要对其index进行调整：将reset_index的drop值设置为False表示重新排index从0开始，一般是不进行调整的，这样可以保持原有的数据索引。

dates =  pd.date_range('20130101',periods=6)
df  = pd.DataFrame(np.random.randn(6,4),index = dates ,columns=list('ABCD'))
print df 
df1  =  df.reindex(index = dates[0:4],columns=list(df.columns)+ ['E'])
df1.loc[dates[0],'E'] =1
df1.loc[dates[2],'E'] =1
print df1
df1 = df1[df1.E== 1].reset_index(drop=False)
print df1

                   A         B         C         D
2013-01-01 -0.181389 -0.088044 -0.459681 -0.201029
2013-01-02  1.675779 -0.554188  0.973400  0.891679
2013-01-03 -1.030581 -0.100559 -0.342108  0.189982
2013-01-04  1.398232 -0.986472 -1.517883  0.358021
2013-01-05  0.321571 -0.629422 -0.032916  0.414784
2013-01-06 -0.482070  0.127791 -0.941574  1.121891
                   A         B         C         D    E
2013-01-01 -0.181389 -0.088044 -0.459681 -0.201029  1.0
2013-01-02  1.675779 -0.554188  0.973400  0.891679  NaN
2013-01-03 -1.030581 -0.100559 -0.342108  0.189982  1.0
2013-01-04  1.398232 -0.986472 -1.517883  0.358021  NaN
       index         A         B         C         D    E
0 2013-01-01 -0.181389 -0.088044 -0.459681 -0.201029  1.0
1 2013-01-03 -1.030581 -0.100559 -0.342108  0.189982  1.0

df1 = df1[df1.E== 1].reset_index(drop=True)
print df1

                   A         B         C         D
2013-01-01  0.413498 -1.059979 -1.023005  0.264287
2013-01-02 -0.483094 -0.573347 -1.563777 -0.098674
2013-01-03  1.807314  0.279142 -1.288200  1.786838
2013-01-04 -0.726217 -0.264770  1.457457  0.556120
2013-01-05 -0.132457  0.241934  0.716555 -0.131015
2013-01-06  0.413555  1.040257 -0.394696  0.270663
                   A         B         C         D    E
2013-01-01  0.413498 -1.059979 -1.023005  0.264287  1.0
2013-01-02 -0.483094 -0.573347 -1.563777 -0.098674  NaN
2013-01-03  1.807314  0.279142 -1.288200  1.786838  1.0
2013-01-04 -0.726217 -0.264770  1.457457  0.556120  NaN
          A         B         C         D    E
0  0.413498 -1.059979 -1.023005  0.264287  1.0
1  1.807314  0.279142 -1.288200  1.786838  1.0

2. preprocessing.LabelEncoder 标签编码

#简单来说 LabelEncoder 是对不连续的数字或者文本进行编号
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit([1,5,67,100])
label = le.transform([1,1,100,67,5])
print label

[0 0 3 2 1]

3. 列表推导式

创建一个包含1到10的平方的列表

squares = []
for x in range(10):
    squares.append(x**2)
print squares
squares = [x**2 for x in range(10)]
print squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

整除3的数字列表

numbers = []
for x in range(10):
    if x % 3 == 0:
        numbers.append(x)
print numbers
numbers = [x for x in range(10) if x % 3 == 0]
print numbers

[0, 3, 6, 9]
[0, 3, 6, 9]

col = ['a','b','c','d']
f =  [x for x in col if x not in ['a','c']]
print f

['b', 'd']

4.concat

可以将数据根据不同的轴作简单的融合

df1 =  pd.DataFrame(np.random.randn(2,3),columns=list('ABC'))
print df1
df2 =  pd.DataFrame(np.random.randn(2,3),index = [5,6],columns=list('ABC'))
print df2
result =  pd.concat([df1,df2])
print result

          A         B        C
0 -2.094518 -0.077125  1.66726
1 -0.044491 -0.000471  0.94399
          A         B         C
5 -0.384184 -0.827485 -1.222335
6  0.344529  0.834145 -0.537666
          A         B         C
0 -2.094518 -0.077125  1.667260
1 -0.044491 -0.000471  0.943990
5 -0.384184 -0.827485 -1.222335
6  0.344529  0.834145 -0.537666

df1 =  pd.DataFrame(np.random.randn(2,3),columns=list('ABC'))
print df1
df2 =  pd.DataFrame(np.random.randn(2,3),columns=list('DEF'))
print df2
result =  pd.concat([df1,df2])
print result

          A         B         C
0  1.385359 -0.924547  0.189359
1  1.530118  1.179600  1.187794
          D         E         F
0 -0.903516 -1.256129  1.525891
1  0.387177 -0.316178  0.837227
          A         B         C         D         E         F
0  1.385359 -0.924547  0.189359       NaN       NaN       NaN
1  1.530118  1.179600  1.187794       NaN       NaN       NaN
0       NaN       NaN       NaN -0.903516 -1.256129  1.525891
1       NaN       NaN       NaN  0.387177 -0.316178  0.837227

df1 =  pd.DataFrame(np.random.randn(2,3),columns=list('ABC'))
print df1
df2 =  pd.DataFrame(np.random.randn(2,3),columns=list('ABC'))
print df2
result =  pd.concat([df1,df2],axis = 1)
print result

          A         B         C
0 -0.090930 -0.529010  2.109190
1 -0.476415 -1.586248 -0.516491
          A         B         C
0  2.063089 -1.651642 -0.538985
1  0.769949  0.436256 -0.466150
          A         B         C         A         B         C
0 -0.090930 -0.529010  2.109190  2.063089 -1.651642 -0.538985
1 -0.476415 -1.586248 -0.516491  0.769949  0.436256 -0.466150