import pandas as pd
ser_obj = pd.Series(range(5), index = ['a', 'b', 'c', 'd', 'e'])
print(ser_obj.head())
a 0
b 1
c 2
d 3
e 4
dtype: int32
# 行索引
print(ser_obj['a'])
print(ser_obj[0])
0
0
# 切片索引
print(ser_obj[1:3])
print(ser_obj['b':'d'])
b 1
c 2
dtype: int32
b 1
c 2
d 3
dtype: int32
# 不连续索引
print(ser_obj[[0, 2, 4]])
print(ser_obj[['a', 'e']])
a 0
c 2
e 4
dtype: int32
a 0
e 4
dtype: int32
# 布尔索引
ser_bool = ser_obj > 2
print(ser_bool)
print(ser_obj[ser_bool])
print(ser_obj[ser_obj > 2])
a False
b False
c False
d True
e True
dtype: bool
d 3
e 4
dtype: int32
d 3
e 4
dtype: int32
import numpy as np
df_obj = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd'])
print(df_obj.head())
a b c d
0 1.487338 -0.131267 0.798305 0.456526
1 0.845968 0.582888 -0.940616 -2.007079
2 0.136282 -0.899088 0.705549 -1.411679
3 -0.416619 -0.379917 0.518185 -0.999199
4 0.945567 -0.548615 -0.265608 -1.448001
# 列索引
print('列索引')
print(df_obj['a']) # 返回Series类型
#print(type(df_obj[[0]])) # 返回DataFrame类型
# 不连续索引
print('不连续索引')
print(df_obj[['a','c']])
#print(df_obj[[1, 3]])
列索引
0 1.487338
1 0.845968
2 0.136282
3 -0.416619
4 0.945567
Name: a, dtype: float64
不连续索引
a c
0 1.487338 0.798305
1 0.845968 -0.940616
2 0.136282 0.705549
3 -0.416619 0.518185
4 0.945567 -0.265608
print(ser_obj)
print(df_obj)
a 0
b 1
c 2
d 3
e 4
dtype: int32
a b c d
0 1.487338 -0.131267 0.798305 0.456526
1 0.845968 0.582888 -0.940616 -2.007079
2 0.136282 -0.899088 0.705549 -1.411679
3 -0.416619 -0.379917 0.518185 -0.999199
4 0.945567 -0.548615 -0.265608 -1.448001
# 标签索引 loc
# Series
print(ser_obj['b':'d'])
print(ser_obj.loc['b':'d'])
# DataFrame
print(df_obj['a'])
print(df_obj.loc[0:2, 'a'])
b 1
c 2
d 3
dtype: int32
b 1
c 2
d 3
dtype: int32
0 1.487338
1 0.845968
2 0.136282
3 -0.416619
4 0.945567
Name: a, dtype: float64
0 1.487338
1 0.845968
2 0.136282
Name: a, dtype: float64
print(ser_obj)
a 0
b 1
c 2
d 3
e 4
dtype: int32
# 整型位置索引 iloc
print(ser_obj['b':'d'])
print(ser_obj.iloc[1:3])
# DataFrame
print(df_obj.iloc[0:2, 0]) # 注意和df_obj.loc[0:2, 'a']的区别
print(df_obj.loc[0:2, 'a'])
b 1
c 2
d 3
dtype: int32
b 1
c 2
dtype: int32
0 1.487338
1 0.845968
Name: a, dtype: float64
0 1.487338
1 0.845968
2 0.136282
Name: a, dtype: float64
print(ser_obj)
a 0
b 1
c 2
d 3
e 4
dtype: int32
# 混合索引 ix
print(ser_obj.ix[1:3])
print(ser_obj.ix['b':'c'])
# DataFrame
print(df_obj.ix[0:2, 0]) # 先按标签索引尝试操作，然后再按位置索引尝试操作
b 1
c 2
dtype: int32
b 1
c 2
dtype: int32
0 1.487338
1 0.845968
2 0.136282
Name: a, dtype: float64
d:\python34\lib\site-packages\ipykernel\__main__.py:2: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
from ipykernel import kernelapp as app
d:\python34\lib\site-packages\ipykernel\__main__.py:6: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
s1 = pd.Series(range(10, 20), index = range(10))
s2 = pd.Series(range(20, 25), index = range(5))
print('s1: ' )
print(s1)
print('')
print('s2: ')
print(s2)
s1:
0 10
1 11
2 12
3 13
4 14
5 15
6 16
7 17
8 18
9 19
dtype: int32
s2:
0 20
1 21
2 22
3 23
4 24
dtype: int32
# Series 对齐运算
s1 + s2
0 30.0
1 32.0
2 34.0
3 36.0
4 38.0
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
dtype: float64
import numpy as np
df1 = pd.DataFrame(np.ones((2,2)), columns = ['a', 'b'])
df2 = pd.DataFrame(np.ones((3,3)), columns = ['a', 'b', 'c'])
print('df1: ')
print(df1)
print('')
print('df2: ')
print(df2)
df1:
a b
0 1.0 1.0
1 1.0 1.0
df2:
a b c
0 1.0 1.0 1.0
1 1.0 1.0 1.0
2 1.0 1.0 1.0
# DataFrame对齐操作
df1 + df2
| a | b | c | |
|---|---|---|---|
| 0 | 2.0 | 2.0 | NaN |
| 1 | 2.0 | 2.0 | NaN |
| 2 | NaN | NaN | NaN |
# 填充未对齐的数据进行运算
print(s1)
print(s2)
s1.add(s2, fill_value = -1)
0 10
1 11
2 12
3 13
4 14
5 15
6 16
7 17
8 18
9 19
dtype: int32
0 20
1 21
2 22
3 23
4 24
dtype: int32
0 30.0
1 32.0
2 34.0
3 36.0
4 38.0
5 14.0
6 15.0
7 16.0
8 17.0
9 18.0
dtype: float64
df1.sub(df2, fill_value = 2.)
| a | b | c | |
|---|---|---|---|
| 0 | 0.0 | 0.0 | 1.0 |
| 1 | 0.0 | 0.0 | 1.0 |
| 2 | 1.0 | 1.0 | 1.0 |
# 填充NaN
s3 = s1 + s2
print(s3)
0 30.0
1 32.0
2 34.0
3 36.0
4 38.0
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
dtype: float64
s3_filled = s3.fillna(-1)
print(s3_filled)
0 30.0
1 32.0
2 34.0
3 36.0
4 38.0
5 -1.0
6 -1.0
7 -1.0
8 -1.0
9 -1.0
dtype: float64
df3 = df1 + df2
print(df3)
a b c
0 2.0 2.0 NaN
1 2.0 2.0 NaN
2 NaN NaN NaN
df3.fillna(100, inplace = True)
print(df3)
a b c
0 2.0 2.0 100.0
1 2.0 2.0 100.0
2 100.0 100.0 100.0
# Numpy ufunc 函数
df = pd.DataFrame(np.random.randn(5,4) - 1)
print(df)
print(np.abs(df))
0 1 2 3
0 -1.278841 -0.441358 -0.918558 1.086543
1 0.355898 -0.841866 -2.527455 0.355916
2 -1.455938 -0.485170 -0.427544 -1.265602
3 -0.509453 -1.181119 -1.544357 -3.855732
4 -1.197029 0.076550 -1.092266 -0.578271
0 1 2 3
0 1.278841 0.441358 0.918558 1.086543
1 0.355898 0.841866 2.527455 0.355916
2 1.455938 0.485170 0.427544 1.265602
3 0.509453 1.181119 1.544357 3.855732
4 1.197029 0.076550 1.092266 0.578271
# 使用apply应用行或列数据
#f = lambda x : x.max()
def f(x):
return x.max()
print(df.apply(f))
0 0.355898
1 0.076550
2 -0.427544
3 1.086543
dtype: float64
# 指定轴方向
print(df.apply(lambda x : x.max(), axis=1))
0 1.086543
1 0.355916
2 -0.427544
3 -0.509453
4 0.076550
dtype: float64
# 使用applymap应用到每个数据
f2 = lambda x : '%.2f' % x
print(df.applymap(f2))
0 1 2 3
0 -1.28 -0.44 -0.92 1.09
1 0.36 -0.84 -2.53 0.36
2 -1.46 -0.49 -0.43 -1.27
3 -0.51 -1.18 -1.54 -3.86
4 -1.20 0.08 -1.09 -0.58
s4 = pd.Series(range(10, 15), index = np.random.randint(5, size=5))
print(s4)
3 10
0 11
0 12
3 13
1 14
dtype: int32
# 索引排序
s4.sort_index(ascending=False)
3 10
3 13
1 14
0 11
0 12
dtype: int32
df4 = pd.DataFrame(np.random.randn(3, 4),
index=np.random.randint(3, size=3),
columns=np.random.randint(4, size=4))
print(df4)
0 3 1 3
1 -1.074869 0.256095 0.136766 -0.606702
2 -1.115481 0.739818 1.292220 0.711678
0 0.245830 -0.144434 -1.078077 0.406480
#df4.sort_index(ascending=False)
df4.sort_index(axis=1)
| 0 | 1 | 3 | 3 | |
|---|---|---|---|---|
| 1 | -1.074869 | 0.136766 | 0.256095 | -0.606702 |
| 2 | -1.115481 | 1.292220 | 0.739818 | 0.711678 |
| 0 | 0.245830 | -1.078077 | -0.144434 | 0.406480 |
# 按值排序
df4.sort_values(by=0)
| 0 | 3 | 1 | 3 | |
|---|---|---|---|---|
| 2 | -1.115481 | 0.739818 | 1.292220 | 0.711678 |
| 1 | -1.074869 | 0.256095 | 0.136766 | -0.606702 |
| 0 | 0.245830 | -0.144434 | -1.078077 | 0.406480 |
df_data = pd.DataFrame([np.random.randn(3), [1., np.nan, np.nan],
[4., np.nan, np.nan], [1., np.nan, 2.]])
df_data.head()
| 0 | 1 | 2 | |
|---|---|---|---|
| 0 | -0.013522 | 0.22717 | -0.202574 |
| 1 | 1.000000 | NaN | NaN |
| 2 | 4.000000 | NaN | NaN |
| 3 | 1.000000 | NaN | 2.000000 |
# isnull
df_data.isnull()
| 0 | 1 | 2 | |
|---|---|---|---|
| 0 | False | False | False |
| 1 | False | True | True |
| 2 | False | True | True |
| 3 | False | True | False |
# dropna
df_data.dropna()
#df_data.dropna(axis=1)
| 0 | 1 | 2 | |
|---|---|---|---|
| 0 | -0.013522 | 0.22717 | -0.202574 |
# fillna
df_data.fillna(-100.)
| 0 | 1 | 2 | |
|---|---|---|---|
| 0 | -0.013522 | 0.22717 | -0.202574 |
| 1 | 1.000000 | -100.00000 | -100.000000 |
| 2 | 4.000000 | -100.00000 | -100.000000 |
| 3 | 1.000000 | -100.00000 | 2.000000 |