导入模块
>>> from EasyPandas import na
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.random.randn(5, 3), index=["a", "c", "e", "f", "h"], columns=["one", "two", "three"])
>>> df["four"] = "bar"
>>> df["five"] = df["one"] > 0
>>> df2 = df.reindex(["a", "b", "c", "d", "e", "f", "g", "h"])DataFrame判断是否为缺失值
>>> res = na(df2, "isna")
>>> print(res) one two three four five
a False False False False False
b True True True True True
c False False False False False
d True True True True True
e False False False False False
f False False False False False
g True True True True True
h False False False False False
Series判断是否为缺失值
>>> res = na(df2["two"], "isna")
>>> print(res)a False
b True
c False
d True
e False
f False
g True
h False
Name: two, dtype: bool
DataFrame判断是否不为缺失值
>>> res = na(df2, "notna")
>>> print(res) one two three four five
a True True True True True
b False False False False False
c True True True True True
d False False False False False
e True True True True True
f True True True True True
g False False False False False
h True True True True True
Series判断是否不为缺失值
>>> res = na(df2["two"], "notna")
>>> print(res)a True
b False
c True
d False
e True
f True
g False
h True
Name: two, dtype: bool
DataFrame填充缺失值为平均数
>>> res = na(df2.iloc[:, :3], "fill")
>>> print(res) one two three
a 0.906106 -0.712913 -0.117904
b -0.154371 -0.159853 0.227454
c -0.912999 2.127544 -0.159811
d -0.154371 -0.159853 0.227454
e -0.755127 -0.585021 0.569916
f 0.497324 -0.645777 0.557569
g -0.154371 -0.159853 0.227454
h -0.507161 -0.983099 0.287500
DataFrame填充缺失值为100
>>> res = na(df2.iloc[:, :3], "fill", fillvalue=100)
>>> print(res) one two three
a 0.906106 -0.712913 -0.117904
b 100.000000 100.000000 100.000000
c -0.912999 2.127544 -0.159811
d 100.000000 100.000000 100.000000
e -0.755127 -0.585021 0.569916
f 0.497324 -0.645777 0.557569
g 100.000000 100.000000 100.000000
h -0.507161 -0.983099 0.287500
Series填充缺失值为平均数
>>> res = na(df2["two"], "fill")
>>> print(res)a -0.712913
b -0.159853
c 2.127544
d -0.159853
e -0.585021
f -0.645777
g -0.159853
h -0.983099
Name: two, dtype: float64
Series填充缺失值为100
>>> res = na(df2["two"], "fill", fillvalue=100)
>>> print(res)a -0.712913
b 100.000000
c 2.127544
d 100.000000
e -0.585021
f -0.645777
g 100.000000
h -0.983099
Name: two, dtype: float64
Series填充缺失值为foo
>>> res = na(df2["four"], "fill", fillvalue="foo")
>>> print(res)a bar
b foo
c bar
d foo
e bar
f bar
g foo
h bar
Name: four, dtype: object
DataFrame向前填充
>>> res = na(df2, "ffill")
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
b 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
d -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
g 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
Series向前填充
>>> res = na(df2, "ffill")
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
b 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
d -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
g 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
Series向前填充
>>> res = na(df2["four"], "ffill")
>>> print(res)a bar
b bar
c bar
d bar
e bar
f bar
g bar
h bar
Name: four, dtype: object
DataFrame向后填充
>>> res = na(df2, "ffill")
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
b 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
d -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
g 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
Series向后填充
>>> res = na(df2, "ffill")
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
b 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
d -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
g 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
Series向后填充
>>> res = na(df2["four"], "ffill")
>>> print(res)a bar
b bar
c bar
d bar
e bar
f bar
g bar
h bar
Name: four, dtype: object
DataFrame删除缺失值，按行删除，只要某行存在缺失值就删除，不原地删除
>>> res = na(df2, "delete")
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
b NaN NaN NaN NaN NaN
c -0.912999 2.127544 -0.159811 bar False
d NaN NaN NaN NaN NaN
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
g NaN NaN NaN NaN NaN
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按行删除，只要某行存在缺失值就删除，原地删除
>>> res = na(df2, "delete", inplace=1)
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
None
DataFrame删除缺失值，按行删除，某行全为缺失值才删除，不原地删除
>>> res = na(df2, "delete", how="all")
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按行删除，某行全为缺失值才删除，原地删除
>>> res = na(df2, "delete", how="all", inplace=1)
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
None
DataFrame删除缺失值，按行删除，只要two列存在缺失值就删除，不原地删除
>>> res = na(df2, "delete", subset="two")
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按行删除，two列和four列全为缺失值才删除，不原地删除
>>> res = na(df2, "delete", subset=["two", "four"], how="all")
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按列删除，某列全为缺失值才删除，不原地删除
>>> res = na(df2, "delete", how="all", bycol=1)
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按列删除，某列存在缺失值就删除，不原地删除
>>> res = na(df2, "delete", bycol=1)
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
DataFrame删除缺失值，按列删除，某列存在缺失值就删除，原地删除
>>> res = na(df2, "delete", bycol=1, inplace=1)
>>> print(df2)
>>> print(res) one two three four five
a 0.906106 -0.712913 -0.117904 bar True
c -0.912999 2.127544 -0.159811 bar False
e -0.755127 -0.585021 0.569916 bar False
f 0.497324 -0.645777 0.557569 bar True
h -0.507161 -0.983099 0.287500 bar False
None
Series删除缺失值，存在缺失值就删除，不原地删除
>>> res = na(df2["two"], "delete")
>>> print(df2["two"])
>>> print(res)a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
Series删除缺失值，全为缺失值才删除，不原地删除（Series只能按照上面的方式删除，这种方法是无法删除的）
>>> res = na(df2["two"], "delete", how="all")
>>> print(df2["two"])
>>> print(res)a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
Series删除缺失值，存在缺失值就删除，原地删除（DataFrame的列是视图，无法原地删除）
>>> res = na(df2["two"], "delete", inplace=1)
>>> print(df2["two"])
>>> print(res)a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
None
Series删除缺失值，存在缺失值就删除，原地删除（DataFrame的列是视图，无法原地删除，先复制才能）
>>> s = df2["two"].copy()
>>> res = na(s, "delete", inplace=1)
>>> print(s)
>>> print(res)a -0.712913
c 2.127544
e -0.585021
f -0.645777
h -0.983099
Name: two, dtype: float64
None