最近做一个系列博客,跟着*学Pandas。
以 pandas作为关键词,在*中进行搜索,随后安照 votes 数目进行排序:
https://*.com/questions/tagged/pandas?sort=votes&pageSize=15
Adding new column to existing DataFrame in Python pandas - Pandas 添加列
https://*.com/questions/12555323/adding-new-column-to-existing-dataframe-in-python-pandas
pandas官方给出了对列的操作, 可以参考:
http://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
- 数据准备
随机生成8*3的DataFrame df1,筛选 a 列大于0.5的行组成df2,作为我们的初始数据。
import numpy as np
import pandas as pd
print pd.__version__
#0.19.2
np.random.seed(0)
df1 = pd.DataFrame(np.random.randn(8, 3), columns=['a', 'b', 'c'])
print df1
a b c
# 0 1.764052 0.400157 0.978738
# 1 2.240893 1.867558 -0.977278
# 2 0.950088 -0.151357 -0.103219
# 3 0.410599 0.144044 1.454274
# 4 0.761038 0.121675 0.443863
# 5 0.333674 1.494079 -0.205158
# 6 0.313068 -0.854096 -2.552990
# 7 0.653619 0.864436 -0.742165
df2 = df1[df1['a']> 0.5]
df3 = df2
sLength = len(df2['a'])
d = pd.Series(np.random.randn(sLength))
直接赋值
采用 df2['d'] = d
或者 df2.loc[:, 'd'] = d
直接进行赋值。
print df2
# a b c
# 0 1.764052 0.400157 0.978738
# 1 2.240893 1.867558 -0.977278
# 2 0.950088 -0.151357 -0.103219
# 4 0.761038 0.121675 0.443863
# 7 0.653619 0.864436 -0.742165
print d
# 0 2.269755
# 1 -1.454366
# 2 0.045759
# 3 -0.187184
# 4 1.532779
print type(d)
#<class 'pandas.core.series.Series'>
# 下面的方法可以,但是会有SettingWithCopyWarning警告
df2['d'] = d
# /Library/Python/2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning:
# A value is trying to be set on a copy of a slice from a DataFrame.
# Try using .loc[row_indexer,col_indexer] = value instead
# See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
# if __name__ == '__main__':
# 为了避免警告我们可以采用这种方式来进行直接赋值
df2.loc[:, 'd'] = d
print df2
a b c d
# 0 1.764052 0.400157 0.978738 2.269755
# 1 2.240893 1.867558 -0.977278 -1.454366
# 2 0.950088 -0.151357 -0.103219 0.045759
# 4 0.761038 0.121675 0.443863 1.532779
# 7 0.653619 0.864436 -0.742165 NaN
df2.loc[:, 'd1'] = d.tolist() # 或者 d.values()
# d.tolist() 返回list
# d.values 返回 numpy.ndarray
print df2
# a b c d d1
# 0 1.764052 0.400157 0.978738 2.269755 2.269755
# 1 2.240893 1.867558 -0.977278 -1.454366 -1.454366
# 2 0.950088 -0.151357 -0.103219 0.045759 0.045759
# 4 0.761038 0.121675 0.443863 1.532779 -0.187184
# 7 0.653619 0.864436 -0.742165 NaN 1.532779
我们可以发现,df2是5行数据, d 也是5个数据,但是赋值之后d列仅有4个值,深究发现,d是Series类型,df2['d'] = d
是根据index对其进行赋值,只有 0 1 2 4 等4个index在d中有对应, 7 没有对应所以为NaN.
如果忽略index影响,我们可以采用d.tolist()
或者 d.values()
同时,在 pandas 0.19.2 中,采用 df2['d'] = d
, 提示SettingWithCopyWarning,尽量避免这种方式,采用df2.loc[:, 'd'] = d
的方式进行列的增加。
assign 赋值
assign可以为DataFrame增加新列,不考虑index问题。
pandas官方参考:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html
print df3
# a b c
# 0 1.764052 0.400157 0.978738
# 1 2.240893 1.867558 -0.977278
# 2 0.950088 -0.151357 -0.103219
# 4 0.761038 0.121675 0.443863
# 7 0.653619 0.864436 -0.742165
print d
# 0 2.269755
# 1 -1.454366
# 2 0.045759
# 3 -0.187184
# 4 1.532779
df3 = df3.assign(d = d.values)
print df3
# a b c d
# 0 1.764052 0.400157 0.978738 2.269755
# 1 2.240893 1.867558 -0.977278 -1.454366
# 2 0.950088 -0.151357 -0.103219 0.045759
# 4 0.761038 0.121675 0.443863 -0.187184
# 7 0.653619 0.864436 -0.742165 1.532779
可以发现 df3 采用 assign 进行赋值,是按照位置顺序赋值,没有考虑index问题。
同时,assign还可以进行多种操作,比如:
df4 = df3.assign(ln_A = lambda x: np.log(x['a']))
print df4
# a b c d ln_A
# 0 1.764052 0.400157 0.978738 2.269755 0.567614
# 1 2.240893 1.867558 -0.977278 -1.454366 0.806875
# 2 0.950088 -0.151357 -0.103219 0.045759 -0.051200
# 4 0.761038 0.121675 0.443863 -0.187184 -0.273072
# 7 0.653619 0.864436 -0.742165 1.532779 -0.425231