-

1.2. ndarray#

+

1.2. ndarray#

NumPy 最核心的数据结构是多维数组（N-dimensional Array）： ndarray。 ndarray 是由同一数据类型的数据组成的数组列表。

diff --git a/docs/ch-numpy/random.html b/docs/ch-numpy/random.html index be0ae58..e752aac 100644 --- a/docs/ch-numpy/random.html +++ b/docs/ch-numpy/random.html @@ -595,7 +595,7 @@

1.4.2.1. 整数

-
array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])
+
array([1, 1, 1, 0, 1, 0, 0, 0, 1, 0])
@@ -608,13 +608,13 @@

1.4.2.1. 整数

-
array([[[4, 2, 4, 3],
-        [2, 0, 4, 1],
-        [2, 4, 2, 2]],
+
array([[[3, 4, 3, 1],
+        [0, 3, 1, 2],
+        [3, 1, 2, 1]],
 
-       [[4, 2, 3, 1],
-        [2, 2, 0, 2],
-        [1, 3, 1, 3]]])
+       [[2, 0, 0, 1],
+        [2, 4, 2, 4],
+        [3, 3, 1, 2]]])
@@ -630,7 +630,7 @@

1.4.2.1. 整数

-
array([1, 1, 7])
+
array([1, 1, 6])
@@ -644,7 +644,7 @@

1.4.2.1. 整数

-
array([5, 7, 6])
+
array([9, 3, 6])
@@ -658,8 +658,8 @@

1.4.2.1. 整数

-
array([[ 7,  4,  5,  8],
-       [19,  8,  8, 11]])
+
array([[ 7,  4,  6,  9],
+       [12,  9,  7, 19]])
@@ -698,11 +698,11 @@

1.4.2.2. 浮点数

-
array([[[0.17116617, 0.61982004, 0.69561155],
-        [0.09851594, 0.58500615, 0.83079726]],
+
array([[[0.14807476, 0.58102001, 0.23324038],
+        [0.16502835, 0.89213102, 0.35402048]],
 
-       [[0.9789088 , 0.34326471, 0.84291916],
-        [0.3305023 , 0.9840284 , 0.92505387]]])
+       [[0.28359726, 0.00118974, 0.82218295],
+        [0.05006935, 0.72554953, 0.66459193]]])
@@ -716,9 +716,9 @@

1.4.2.2. 浮点数

-
array([[-4.86570018, -1.86428096],
-       [-1.09820306, -1.24210335],
-       [-4.92156741, -3.23912907]])
+
array([[-1.75693239, -1.01660529],
+       [-4.33400607, -4.04224085],
+       [-3.17906271, -2.74511231]])
@@ -741,13 +741,13 @@

1.4.2.3. 排列 
===before shuffle===
-[[240 249]
- [357 144]
- [384 310]]
+[[367 392]
+ [142 349]
+ [113 298]]
 ===after shuffle===
-[[357 144]
- [384 310]
- [240 249]]
+[[367 392]
+ [142 349]
+ [113 298]]
@@ -760,7 +760,7 @@

1.4.2.3. 排列

-
array([6, 0, 5, 9, 7, 2, 1, 4, 8, 3])
+
array([9, 7, 4, 0, 1, 2, 6, 5, 8, 3])
@@ -772,7 +772,7 @@

1.4.2.3. 排列

-
array([ 4,  9, 15,  1, 12])
+
array([ 4, 15, 12,  1,  9])
@@ -785,9 +785,9 @@

1.4.2.3. 排列

-
array([[175, 264],
-       [104, 120],
-       [185, 243]])
+
array([[328, 137],
+       [150, 296],
+       [273, 106]])
@@ -804,9 +804,9 @@

1.4.2.3. 排列

-
array([[ 3,  5,  6,  4,  9],
-       [10, 13,  0,  8,  1],
-       [12,  5, 10,  8,  1]])
+
array([[11, 11,  0,  5,  6],
+       [14, 12,  4,  5, 12],
+       [ 7,  2,  6,  3, 10]])
@@ -820,9 +820,9 @@

1.4.2.3. 排列

-
array([[ 6,  9,  3,  5,  4],
-       [13,  0,  1, 10,  8],
-       [ 8,  5, 12,  1, 10]])
+
array([[ 0,  5, 11, 11,  6],
+       [12,  4, 12, 14,  5],
+       [ 3,  7, 10,  6,  2]])
@@ -836,9 +836,9 @@

1.4.2.3. 排列

-
array([[12, 13,  0,  8,  1],
-       [ 3,  5,  6,  8,  9],
-       [10,  5, 10,  4,  1]])
+
array([[11, 12,  6,  5, 12],
+       [ 7,  2,  0,  3,  6],
+       [14, 11,  4,  5, 10]])
@@ -851,9 +851,9 @@

1.4.2.3. 排列

-
array([[ 3,  5,  6,  4,  9],
-       [10, 13,  0,  8,  1],
-       [12,  5, 10,  8,  1]])
+
array([[11, 11,  0,  5,  6],
+       [14, 12,  4,  5, 12],
+       [ 7,  2,  6,  3, 10]])
@@ -867,9 +867,9 @@

1.4.2.3. 排列

-
array([[ 5,  9,  6,  4,  3],
-       [10,  8, 13,  1,  0],
-       [ 1,  8, 10, 12,  5]])
+
array([[11, 11,  6,  5,  0],
+       [ 4,  5, 12, 14, 12],
+       [ 7,  6,  3,  2, 10]])
@@ -917,7 +917,7 @@

1.4.3.1.1. 二项分布 -
0.3898
+
0.3884
@@ -937,7 +937,7 @@

1.4.3.1.2. 泊松分布 -
array([6, 6, 2, ..., 2, 5, 3])
+
array([1, 4, 2, ..., 0, 2, 1])
@@ -958,65 +958,65 @@

1.4.3.2. 超几何分布 -
array([ 8, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,  9,  8, 10,
-       10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9,  9, 10, 10, 10,
-       10,  9,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10,
-       10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10,
-       10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
-        9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10,  9, 10, 10, 10, 10,  9, 10, 10,  9,  9,  9,  9, 10, 10, 10,  9,
-       10,  9,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10,
-        9, 10,  9, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10,
-       10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,  8,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
-       10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9, 10,
-       10, 10,  9, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9,  9, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10,
+
array([10,  9, 10, 10, 10, 10, 10, 10, 10,  9,  8, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10, 10, 10,  9,  9,
        10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+        9, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,
+       10, 10, 10,  9,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,  9,
+       10, 10, 10, 10, 10,  9, 10,  9, 10,  9,  9, 10, 10,  9, 10, 10, 10,
+       10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9,  9, 10,
+       10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10,  9,  9, 10,  9,
+       10,  9, 10, 10, 10, 10, 10,  9,  9, 10, 10, 10,  9, 10, 10, 10, 10,
+       10,  9, 10, 10, 10, 10,  9,  9, 10, 10, 10, 10,  9, 10, 10, 10, 10,
+       10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
+        9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10,  9, 10,  9, 10, 10, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10,
+        9, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10,  9,  9,  9, 10, 10,  9, 10, 10,  9, 10, 10,
+       10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10,  9,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,
+       10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,
+       10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,
+       10,  9, 10, 10, 10, 10, 10,  9, 10,  9,  9, 10, 10, 10,  9, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10,
+       10, 10, 10,  9, 10, 10, 10,  9,  9, 10,  9,  9, 10,  9, 10, 10,  9,
+       10,  9, 10, 10, 10,  9, 10, 10,  9, 10,  8, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,
+       10, 10, 10, 10,  9, 10,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9,
+        9, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,
+        9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10,
+       10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10, 10,
        10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-        9, 10, 10,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10,
-       10, 10,  9, 10,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
-       10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,  9, 10, 10, 10, 10, 10,
-       10, 10,  9, 10,  9, 10, 10, 10,  9,  9, 10, 10,  9, 10, 10, 10, 10,
-        9, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9, 10, 10, 10,  9,  9,
-       10, 10, 10,  9, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,
-       10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10,
-       10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  8,
-       10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
-        9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10,
-       10, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-        9, 10,  9, 10, 10,  9,  9, 10, 10, 10,  9, 10, 10, 10, 10,  8, 10,
-        9, 10,  8,  9,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9,  9,
-       10, 10,  9,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,
-       10, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10,
-       10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10,
-       10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10,
-       10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,  9,
-       10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,
-       10,  9,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10,  9, 10, 10,  9, 10,  9, 10,  9, 10,  9, 10,  9, 10, 10, 10,
-        9, 10, 10, 10, 10, 10, 10,  9,  9,  9, 10, 10, 10, 10, 10, 10, 10,
-       10, 10, 10, 10, 10,  9, 10, 10,  9,  9, 10,  9, 10, 10, 10, 10, 10,
-        9, 10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10,  9,  9,
-       10,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10, 10, 10,  9, 10,
+        9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10,
+       10, 10,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+       10,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9, 10, 10,
+       10, 10, 10,  9, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10,
+       10,  9, 10, 10, 10, 10, 10,  9,  9, 10, 10,  9,  9, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+        9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10,  9, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10,  9, 10,  9, 10,  9, 10, 10, 10, 10,  9,
+       10,  8, 10, 10, 10,  9, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10,
+       10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9,
+       10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10,
+        9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
        10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10,  9, 10,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
-       10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10,  9,  9, 10,
-       10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-       10, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10,
-       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
-        9, 10, 10, 10,  9, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,
-       10, 10, 10, 10, 10, 10,  8, 10,  9, 10, 10, 10,  9, 10, 10, 10, 10,
-        9, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9, 10])
+       10, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  8, 10, 10, 10,  9, 10,
+       10, 10, 10, 10, 10,  9, 10,  9, 10, 10,  9, 10, 10, 10, 10, 10, 10,
+       10, 10,  9, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,  9, 10,  9,
+       10,  9, 10, 10,  9, 10, 10,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  8,
+       10, 10,  9, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10,  9, 10, 10,
+       10, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10,
+       10, 10, 10,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,  9, 10,
+       10,  9, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  9,
+       10, 10, 10,  9, 10, 10, 10, 10, 10, 10, 10,  9, 10, 10])
@@ -1030,7 +1030,7 @@

1.4.3.2. 超几何分布 -
0.0028
+
0.00309
@@ -1064,8 +1064,8 @@

1.4.3.3.2. 正态分布 -
array([[ 0.454937  ,  1.67666093,  5.80110142,  0.08253836],
-       [ 4.77680903,  5.14700826, -2.61894703,  6.47941423]])
+
array([[2.61265572, 2.04603815, 3.7268495 , 6.90379907],
+       [5.19359562, 6.69240097, 3.06642968, 4.71226909]])
@@ -1097,7 +1097,7 @@

1.4.3.3.3. 指数分布 -
0.08199999999999996
+
0.07710000000000006
@@ -1119,11 +1119,11 @@

1.4.4.1. 均匀分布 -
array([[[0.50044277, 0.04294602, 0.73130176],
-        [0.91687144, 0.55942646, 0.12681375]],
+
array([[[0.42501463, 0.9943867 , 0.78190126],
+        [0.65178824, 0.23446072, 0.99763473]],
 
-       [[0.78626745, 0.71919683, 0.55507281],
-        [0.12873362, 0.94793894, 0.67462743]]])
+       [[0.26039533, 0.42281102, 0.13389823],
+        [0.30273457, 0.7322349 , 0.67729695]]])
@@ -1140,11 +1140,11 @@

1.4.4.2. 正态分布 -
array([[[-0.72070022,  0.78897801, -1.28885247],
-        [ 1.18516225, -0.33193839,  0.61640163]],
+
array([[[-1.54919887,  1.23974936, -0.06492921],
+        [ 2.00327028,  0.49818766, -0.48685538]],
 
-       [[ 0.18973705, -0.66455947,  1.44364126],
-        [-1.14987731, -0.69073724,  0.86121259]]])
+       [[ 0.21497762,  1.86931969,  0.05804312],
+        [ 0.67437435,  0.77588613,  0.29496005]]])
@@ -1160,13 +1160,13 @@

1.4.4.3. 数值范围 -
array([[[294, 139, 261, 205],
-        [396, 301, 355, 206],
-        [339, 204, 359, 318]],
+
array([[[240, 155, 239, 266],
+        [360, 103, 236, 374],
+        [140, 118, 341, 166]],
 
-       [[331, 135, 291, 162],
-        [314, 130, 205, 268],
-        [221, 189, 270, 308]]])
+       [[113, 232, 173, 288],
+        [227, 238, 363, 244],
+        [223, 277, 295, 320]]])
diff --git a/docs/ch-pandas/data-preprocessing.html b/docs/ch-pandas/data-preprocessing.html index 81e6ba0..f197e19 100644 --- a/docs/ch-pandas/data-preprocessing.html +++ b/docs/ch-pandas/data-preprocessing.html @@ -1725,7 +1725,7 @@

2.4.4. 更改 DataFrame -
/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_63127/832431325.py:1: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
+
/var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_34083/832431325.py:1: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
   df.applymap(lambda x : round(x, 2) if type(x)!=str else x)
 

 

diff --git a/docs/ch-pandas/dataframe-groupby.html b/docs/ch-pandas/dataframe-groupby.html
index bc0a10f..92f3087 100644
--- a/docs/ch-pandas/dataframe-groupby.html
+++ b/docs/ch-pandas/dataframe-groupby.html
@@ -408,11 +408,11 @@ 
 Contents 

                   
   

 
2.5. 分组汇总#

-
实际的数据分析中，经常需要对某一类数据进行统计分析。比如，假如我们拥有全国所有人的身高和体重数据，我们想按照省份分组，统计每个省的平均身高和平均体重，这时候就需要使用分组操作。pandas 提供了 groupby 函数进行类似的分组汇总操作。图 2.1 计算平均身高的分组汇总流程，主要包括两部分：分组与汇总。其中分组阶段将同一类的内容归结到相同的组中；汇总阶段将所关心的数据进行计算，比如求和、求平均等。

+
实际的数据分析中，经常需要对某一类数据进行统计分析。比如，假如我们拥有全国所有人的身高和体重数据，我们想按照省份分组，统计每个省的平均身高和平均体重，这时候就需要使用分组操作。pandas 提供了 groupby 函数进行类似的分组汇总操作。图 2.7 计算平均身高的分组汇总流程，主要包括两部分：分组与汇总。其中分组阶段将同一类的内容归结到相同的组中；汇总阶段将所关心的数据进行计算，比如求和、求平均等。

 
按哪些字段进行分组，这些字段又被成为 ** 分组变量 **。对其他字段进行汇总，其他汇总字段被成为 ** 汇总变量 **。对汇总变量进行计算，被称为 ** 汇总统计量 **。

 

 ../_images/groupby.svg

-
图 2.1 分组与汇总#

+
图 2.7 分组与汇总#

 

 

 

@@ -554,31 +554,342 @@ 
2.5.2. 汇总变量例如按照 year 汇总 tcgdp 和 POP，在 .groupby 后直接使用 [] 筛选相应列，再接汇总统计量。

 

 

-
df.groupby(['year'])['tcgdp','POP'].mean()
+
df.groupby(['year'])[['tcgdp','POP']].mean()
 

 

 

 

-
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-Cell In[3], line 1
-----> 1 df.groupby(['year'])['tcgdp','POP'].mean()
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/generic.py:1960, in DataFrameGroupBy.__getitem__(self, key)
-   1956 # per GH 23566
-   1957 if isinstance(key, tuple) and len(key) > 1:
-   1958     # if len == 1, then it becomes a SeriesGroupBy and this is actually
-   1959     # valid syntax, so don't raise
--> 1960     raise ValueError(
-   1961         "Cannot subset columns with a tuple with more than one element. "
-   1962         "Use a list instead."
-   1963     )
-   1964 return super().__getitem__(key)
-
-ValueError: Cannot subset columns with a tuple with more than one element. Use a list instead.
-

-

-

+

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+
tcgdpPOP
year
19503954.39446716239.392063
19514541.91701116461.731179
19525150.94693016730.487389
19535483.99531017019.548668
19545692.81177517326.304137
19556196.46547717657.801874
19566640.26485117999.434226
19577104.04112818364.700442
19587528.45460518730.439042
19598148.51089119043.260395
19608828.60577619242.437274
19619128.45587919423.141142
19629687.30708219760.533626
196310351.91777520233.695226
196411269.68006120719.860558
196512105.69127321182.282832
196613176.72281321655.463121
196714051.08423522121.536947
196815421.58474022606.150837
196917218.75898723113.133937
197021282.56727923618.829389
197123432.91068924137.869711
197225833.37931024644.483005
197329159.64660025140.115900
197432601.67805025623.884663
197536109.97016326088.471411
197640165.94772226535.470116
197744691.03377726979.055474
197850393.15225127418.046916
197956983.54096327873.890479
198063850.74860228329.849105
198170997.23187728790.876000
198275786.11696629287.531784
198381059.06606429795.689879
198488303.03198430288.007526
198594477.34288730791.765868
1986101596.41421531313.561847
1987109494.18351731863.784021
1988118048.07842132417.290021
1989126221.57949332964.028537
1990144220.07533333516.168453
1991151894.23435834055.531837
1992159301.70586334567.649100
1993168690.98070435065.424347
1994178947.61326935548.469784
1995190126.58030436030.590795
1996200132.76611636513.694642
1997210946.88984336991.605037
1998216481.94852037462.231458
1999228114.84029137920.696968
2000244206.60178438307.409353
2001255312.48706238742.535742
2002269661.40928639176.028268
2003286959.70488539606.837547
2004312186.33435040040.118863
2005344724.42019540475.047642
2006379205.67431740909.750389
2007416504.77566941347.240695
2008446441.98826241731.031032
2009448186.87112742174.566563

+

 

 

 

@@ -712,7 +1023,7 @@ 
2.5.4. 多重索引例如，按照 country 和 year 顺序对 tcgdp 和 POP 进行分组汇总，汇总统计量为最小值和最大值。

 

 

-
df.groupby(['country','year'])['tcgdp','POP'].agg(['min','max'])
+
df.groupby(['country','year'])[['tcgdp','POP']].agg(['min','max'])
 

 

 

@@ -850,7 +1161,7 @@ 
2.5.4. 多重索引例如，查询各个国家 country 各年 year 人口 POP 的最小值。

 

 

-
df_query = df.groupby(['country','year'])['tcgdp','POP'].agg(['min','max'])
+
df_query = df.groupby(['country','year'])[['tcgdp','POP']].agg(['min','max'])
 df_query['POP']['min']
 

 

diff --git a/docs/ch-pandas/dataframe-merge-concat.html b/docs/ch-pandas/dataframe-merge-concat.html
index 561f038..f5dc43c 100644
--- a/docs/ch-pandas/dataframe-merge-concat.html
+++ b/docs/ch-pandas/dataframe-merge-concat.html
@@ -414,10 +414,10 @@ 
2.6. 多表操作之前的操作主要在单个 DataFrame ，实际上，我们经常需要对多个 DataFrame 联合起来进行分析。pandas 提供了多 DataFrame 之间的合并和连接的操作，分别是 merge() 和 concat() 函数。比如，我们可以将两个 DataFrame 合并成一个，且保留所有的列。

 

 
2.6.1. merge#

-
pandas 的 merge 操作可以合并两个 DataFrame（或者称为表） ，类似于 SQL 中的 JOIN 操作。 我们可以想象成：一个大表被拆分成两个小表，两个小表都包含一些同样的数据。现在我们需要把两个小表合并，生成一个大表，大表包含了两个小表的字段。

+
pandas 的 merge() 操作可以合并两个 DataFrame（或者称为表） ，类似于 SQL 中的 JOIN 操作。 我们可以想象成：一个大表被拆分成两个小表，两个小表都包含一些同样的数据。现在我们需要把两个小表合并，生成一个大表，大表包含了两个小表的字段。

 

 ../_images/merge.svg

-
图 2.2 对两个 DataFrame 进行 merge 操作#

+
图 2.8 对两个 DataFrame 进行 merge 操作#

 

 

 

@@ -942,7 +942,13 @@ 
2.6.1.2. 其他合并方式
 
2.6.2. concat#

-
与 merge 对表结构进行横向的操作不同，concat 函数默认是进行纵向的合并，也就是将多行进行 ** 堆叠式 ** 的合并。

+
与 merge() 对表结构进行横向的操作不同，concat() 函数默认是进行纵向的合并，也就是将多行进行 堆叠式 的合并，如 图 2.9 所示。

+

+../_images/concat.svg
+

+
图 2.9 对两个 DataFrame 进行 concat 操作#

+

+

 
比如下面的例子，把所有的行和所有的列合并在一起，缺失的填充 NaN。

 

 

diff --git a/docs/ch-pandas/dataframe-pivot-melt.html b/docs/ch-pandas/dataframe-pivot-melt.html
index b288ea0..adbcff1 100644
--- a/docs/ch-pandas/dataframe-pivot-melt.html
+++ b/docs/ch-pandas/dataframe-pivot-melt.html
@@ -418,6 +418,13 @@ 
2.7. 数据透视
 
2.7.1. pivot#

 
在使用 Excel 这样的表格时，一个高阶的操作是使用“数据透视表”对数据进行分类汇总。pandas 的 pivot() 提供与 Excel 对应的数据透视的功能，它根据一个或多个列的值对数据进行重新排列和汇总，以便更好地理解数据的结构和关系。pivot() 能够可自定义函数进行聚合，因此功能更强大。

+
图 2.10 是一个数据透视的示意图，下文还将展示具体的例子。

+

+../_images/pivot.svg
+

+
图 2.10 数据透视#

+

+

 

 

 
@@ -693,219 +700,110 @@ 
2.7.1.1. index 和 columns 参数
index 生成透视表的行索引，可以为多个属性。

 
  • columns 生成透视表的列索引。
    • 
 
-
    例 1: 按照 Name 分组，即添加 Name 的行索引。这个功能与 groupby 很像。这个例子中，aggfunc 参数没有设置，默认求平均值。
    
+
    例 1: 按照 Name 分组，即添加 Name 的行索引。这个功能与 groupby() 很像。这个例子中，aggfunc 参数没有设置，默认求平均值。
    
 
    
 
    
-
    pd.pivot_table(sales_df, index=['Name'])
+
    pd.pivot_table(sales_df, index=['Name'], values=['Quantity', 'Price'])
 
    
 
    
 
    
 
    
-
    ---------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1874, in GroupBy._agg_py_fallback(self, how, values, ndim, alt)
-   1873 try:
--> 1874     res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
-   1875 except Exception as err:
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/ops.py:850, in BaseGrouper.agg_series(self, obj, func, preserve_dtype)
-    848     preserve_dtype = True
---> 850 result = self._aggregate_series_pure_python(obj, func)
-    852 npvalues = lib.maybe_convert_objects(result, try_float=False)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/ops.py:871, in BaseGrouper._aggregate_series_pure_python(self, obj, func)
-    870 for i, group in enumerate(splitter):
---> 871     res = func(group)
-    872     res = extract_result(res)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:2380, in GroupBy.mean.<locals>.<lambda>(x)
-   2377 else:
-   2378     result = self._cython_agg_general(
-   2379         "mean",
--> 2380         alt=lambda x: Series(x).mean(numeric_only=numeric_only),
-   2381         numeric_only=numeric_only,
-   2382     )
-   2383     return result.__finalize__(self.obj, method="groupby")
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/series.py:6221, in Series.mean(self, axis, skipna, numeric_only, **kwargs)
-   6213 @doc(make_doc("mean", ndim=1))
-   6214 def mean(
-   6215     self,
-   (...)
-   6219     **kwargs,
-   6220 ):
--> 6221     return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/generic.py:11984, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
-  11977 def mean(
-  11978     self,
-  11979     axis: Axis | None = 0,
-   (...)
-  11982     **kwargs,
-  11983 ) -> Series | float:
-> 11984     return self._stat_function(
-  11985         "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
-  11986     )
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/generic.py:11941, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
-  11939 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
-> 11941 return self._reduce(
-  11942     func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
-  11943 )
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/series.py:6129, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
-   6125     raise TypeError(
-   6126         f"Series.{name} does not allow {kwd_name}={numeric_only} "
-   6127         "with non-numeric dtypes."
-   6128     )
--> 6129 return op(delegate, skipna=skipna, **kwds)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/nanops.py:147, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
-    146 else:
---> 147     result = alt(values, axis=axis, skipna=skipna, **kwds)
-    149 return result
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/nanops.py:404, in _datetimelike_compat.<locals>.new_func(values, axis, skipna, mask, **kwargs)
-    402     mask = isna(values)
---> 404 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
-    406 if datetimelike:
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/nanops.py:720, in nanmean(values, axis, skipna, mask)
-    719 the_sum = values.sum(axis, dtype=dtype_sum)
---> 720 the_sum = _ensure_numeric(the_sum)
-    722 if axis is not None and getattr(the_sum, "ndim", False):
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/nanops.py:1693, in _ensure_numeric(x)
-   1691 if isinstance(x, str):
-   1692     # GH#44008, GH#36703 avoid casting e.g. strings to numeric
--> 1693     raise TypeError(f"Could not convert string '{x}' to numeric")
-   1694 try:
-
-TypeError: Could not convert string 'John Smith' to numeric
-
-The above exception was the direct cause of the following exception:
-
-TypeError                                 Traceback (most recent call last)
-Cell In[3], line 1
-----> 1 pd.pivot_table(sales_df, index=['Name'])
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/reshape/pivot.py:99, in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
-     96     table = concat(pieces, keys=keys, axis=1)
-     97     return table.__finalize__(data, method="pivot_table")
----> 99 table = __internal_pivot_table(
-    100     data,
-    101     values,
-    102     index,
-    103     columns,
-    104     aggfunc,
-    105     fill_value,
-    106     margins,
-    107     dropna,
-    108     margins_name,
-    109     observed,
-    110     sort,
-    111 )
-    112 return table.__finalize__(data, method="pivot_table")
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/reshape/pivot.py:169, in __internal_pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
-    166     values = list(values)
-    168 grouped = data.groupby(keys, observed=observed, sort=sort, dropna=dropna)
---> 169 agged = grouped.agg(aggfunc)
-    171 if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
-    172     agged = agged.dropna(how="all")
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/generic.py:1445, in DataFrameGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
-   1442     kwargs["engine_kwargs"] = engine_kwargs
-   1444 op = GroupByApply(self, func, args=args, kwargs=kwargs)
--> 1445 result = op.agg()
-   1446 if not is_dict_like(func) and result is not None:
-   1447     # GH #52849
-   1448     if not self.as_index and is_list_like(func):
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/apply.py:172, in Apply.agg(self)
-    169 kwargs = self.kwargs
-    171 if isinstance(func, str):
---> 172     return self.apply_str()
-    174 if is_dict_like(func):
-    175     return self.agg_dict_like()
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/apply.py:586, in Apply.apply_str(self)
-    584         else:
-    585             self.kwargs["axis"] = self.axis
---> 586 return self._apply_str(obj, func, *self.args, **self.kwargs)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/apply.py:669, in Apply._apply_str(self, obj, func, *args, **kwargs)
-    667 f = getattr(obj, func)
-    668 if callable(f):
---> 669     return f(*args, **kwargs)
-    671 # people may aggregate on a non-callable attribute
-    672 # but don't let them think they can pass args to it
-    673 assert len(args) == 0
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:2378, in GroupBy.mean(self, numeric_only, engine, engine_kwargs)
-   2371     return self._numba_agg_general(
-   2372         grouped_mean,
-   2373         executor.float_dtype_mapping,
-   2374         engine_kwargs,
-   2375         min_periods=0,
-   2376     )
-   2377 else:
--> 2378     result = self._cython_agg_general(
-   2379         "mean",
-   2380         alt=lambda x: Series(x).mean(numeric_only=numeric_only),
-   2381         numeric_only=numeric_only,
-   2382     )
-   2383     return result.__finalize__(self.obj, method="groupby")
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1929, in GroupBy._cython_agg_general(self, how, alt, numeric_only, min_count, **kwargs)
-   1926     result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
-   1927     return result
--> 1929 new_mgr = data.grouped_reduce(array_func)
-   1930 res = self._wrap_agged_manager(new_mgr)
-   1931 out = self._wrap_aggregated_output(res)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/internals/managers.py:1428, in BlockManager.grouped_reduce(self, func)
-   1424 if blk.is_object:
-   1425     # split on object-dtype blocks bc some columns may raise
-   1426     #  while others do not.
-   1427     for sb in blk._split():
--> 1428         applied = sb.apply(func)
-   1429         result_blocks = extend_blocks(applied, result_blocks)
-   1430 else:
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/internals/blocks.py:366, in Block.apply(self, func, **kwargs)
-    360 @final
-    361 def apply(self, func, **kwargs) -> list[Block]:
-    362     """
-    363     apply the function to my values; return a block if we are not
-    364     one
-    365     """
---> 366     result = func(self.values, **kwargs)
-    368     result = maybe_coerce_values(result)
-    369     return self._split_op_result(result)
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1926, in GroupBy._cython_agg_general.<locals>.array_func(values)
-   1923 else:
-   1924     return result
--> 1926 result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
-   1927 return result
-
-File ~/anaconda3/envs/pyds/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1878, in GroupBy._agg_py_fallback(self, how, values, ndim, alt)
-   1876     msg = f"agg function failed [how->{how},dtype->{ser.dtype}]"
-   1877     # preserve the kind of exception that raised
--> 1878     raise type(err)(msg) from err
-   1880 if ser.dtype == object:
-   1881     res_values = res_values.astype(object, copy=False)
-
-TypeError: agg function failed [how->mean,dtype->object]
-
    
-
    
-
    
+
    
+
+
+  
+    
+      
+      
+      
+    
+    
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+    
+  
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+  
+
    PriceQuantity
    Name
    Barton LLC35000.01.000000
    Fritsch, Russel and Anderson35000.01.000000
    Herman LLC65000.02.000000
    Jerde-Hilpert5000.02.000000
    Kassulke, Ondricka and Metz7000.03.000000
    Keeling LLC100000.05.000000
    Kiehn-Spinka65000.02.000000
    Koepp Ltd35000.02.000000
    Kulas Inc25000.01.500000
    Purdy-Kunde30000.01.000000
    Stokes LLC7500.01.000000
    Trantow-Barrows15000.01.333333
    
+
    
 
    
-
    例 2: 按 Manager 和 Rep 分组，类似于使用 groupby 对两个字段 Manager 和 Rep 进行分组。
    
+
    例 2: 按 Manager 和 Rep 分组，类似于使用 groupby() 对两个字段 Manager 和 Rep 进行分组。
    
 
    
 
    
-
    pd.pivot_table(sales_df, index=['Manager','Rep'])
+
    pd.pivot_table(sales_df, index=['Manager', 'Rep'], values=['Quantity', 'Price'])
 
    
 
    
 
    
@@ -929,7 +827,6 @@ 
    2.7.1.1. index 和 columns 参数
       
       
-      Account
       Price
       Quantity
     
@@ -938,39 +835,33 @@ 
    2.7.1.1. index 和 columns 参数Rep
       
       
-      
     
   
   
     
       Debra Henley
       Craig Booker
-      720237.0
       20000.000000
       1.250000
     
     
       Daniel Hilton
-      194874.0
       38333.333333
       1.666667
     
     
       John Smith
-      576220.0
       20000.000000
       1.500000
     
     
       Fred Anderson
       Cedric Moss
-      196016.5
       27500.000000
       1.250000
     
     
       Wendy Yule
-      614061.5
       44250.000000
       3.000000
     
@@ -981,7 +872,7 @@ 
    2.7.1.1. index 和 columns 参数前面两个例子与 groupby 很像，第 3 个例子，增加 columns 这个参数，添加透视表的列 Product，把原来存在于 Product 中的数据作为列名提出来。
    
 
    
 
    
-
    pd.pivot_table(sales_df, index=['Manager','Rep'], columns=["Product"])
+
    pd.pivot_table(sales_df, index=['Manager','Rep'], values=['Quantity', 'Price'], columns=["Product"])
 
    
 
    
 
    
@@ -1009,7 +900,6 @@ 
    2.7.1.1. index 和 columns 参数
       
       
-      Account
       Price
       Quantity
     
@@ -1024,10 +914,6 @@ 
    2.7.1.1. index 和 columns 参数Maintenance
       Monitor
       Software
-      CPU
-      Maintenance
-      Monitor
-      Software
     
     
       Manager
@@ -1040,20 +926,12 @@ 
    2.7.1.1. index 和 columns 参数
       
       
-      
-      
-      
-      
     
   
   
     
       Debra Henley
       Craig Booker
-      726008.0
-      714466.0
-      NaN
-      714466.0
       32500.0
       5000.0
       NaN
@@ -1065,10 +943,6 @@ 
    2.7.1.1. index 和 columns 参数
     
       Daniel Hilton
-      182863.5
-      NaN
-      NaN
-      218895.0
       52500.0
       NaN
       NaN
@@ -1080,10 +954,6 @@ 
    2.7.1.1. index 和 columns 参数
     
       John Smith
-      740150.0
-      412290.0
-      NaN
-      NaN
       35000.0
       5000.0
       NaN
@@ -1096,10 +966,6 @@ 
    2.7.1.1. index 和 columns 参数
       Fred Anderson
       Cedric Moss
-      152689.0
-      239344.0
-      NaN
-      239344.0
       47500.0
       5000.0
       NaN
@@ -1111,10 +977,6 @@ 
    2.7.1.1. index 和 columns 参数
     
       Wendy Yule
-      709407.0
-      307599.0
-      729833.0
-      NaN
       82500.0
       7000.0
       5000.0
@@ -1128,7 +990,7 @@ 
    2.7.1.1. index 和 columns 参数
 
    
 
    
-
    生成的数据透视表有两层列索引，第一层是原有的 Account、Price、Quantity 这些字段，第二层是 columns=["Product"] 里面的值：CPU、Monitor 等。
    
+
    生成的数据透视表有两层列索引，第一层是原有的 Price、Quantity 这些字段，第二层是 columns=["Product"] 里面的值：CPU、Monitor 等。
    
 
    
 
    
 
    2.7.1.2. values 参数#
    
@@ -1136,7 +998,7 @@ 
    2.7.1.2. values 参数
 
    
-
    pd.pivot_table(sales_df,index=['Manager','Rep'], values='Price')
+
    pd.pivot_table(sales_df, index=['Manager','Rep'], values='Price')
 
    
 
    
 
    
@@ -1207,6 +1069,10 @@ 
    2.7.1.3. aggfunc 参数
+
    /var/folders/4n/v40br47s46ggrjm9bdm64lwh0000gn/T/ipykernel_42863/1265594602.py:1: FutureWarning: The provided callable <function mean at 0x10f8080e0> is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
+  pd.pivot_table(sales_df, index=["Manager","Rep"], values=["Price"], aggfunc=[np.mean, len])
+
    
+
    
 
    
 
+
+  
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+  
+
    Column1Column2
    count2.0000002.000000
    mean1.5000003.500000
    std0.7071070.707107
    min1.0000003.000000
    25%1.2500003.250000
    50%1.5000003.500000
    75%1.7500003.750000
    max2.0000004.000000
    
+
    
+
    
+
      
+
    • 使用 DataFrame.agg() 方法计算特定的统计量组合。
      • 
+
    
+
    有时候我们对一列需要指定的一些统计量，对不同列又有不同的需求，则可以使用 DataFrame.agg() 方法，传入字典参数，key 为列名，value 为需要的统计量的列表。
    
+
    例：对 df 的第一列获得最小值 / 最大值 / 中位数 / 偏度，对第二列获得最小值 / 最大值 / 中位数 / 均值。
    
+
    
+
    
+
    df.agg({
+    'Column1':['min','max','median','skew'],
+    'Column2':['min','max','median','mean']   
+})
+
    
+
    
+
    
+
    
+
    
+
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+
    Column1Column2
    min1.03.0
    max2.04.0
    median1.53.5
    skewNaNNaN
    meanNaN3.5
    
+
    
+
    
 
    
 
    
 
    
@@ -631,8 +841,8 @@ 
    2.2.3. 案例：PWT
    -
    -

    2.2.3.1. 查看数据#

    +
    +

    2.2.3.1. 查看数据#

    • 使用 read_csv() 读取数据。

    @@ -1131,6 +1341,22 @@

    2.2.3.1. 查看数据 +

  • rename() 函数既可以用于更改行标签，也可以用于列标签。传入一个字典，其中键为当前名称，值为新名称，以更新相应的名称。

    • + +

    例：

    +
      +

    1. 将 year 改为 Year，country 改为 Country：

      2. +
    +
    df_renamed = df.rename(columns={'year':Year, 'country':'Country'})
+
    +
    +
      +

    1. 将所有列名改为小写：

      2. +
    +
    df_renamed = df.rename(columns=str.lower)
+
    +
    +

    • .index 查看数据框行名。

    @@ -1235,10 +1461,11 @@

    2.2.3.1. 查看数据2.2.1. Series
  • 2.2.2. DataFrame
  • 2.2.3. 案例：PWT
    • diff --git a/docs/objects.inv b/docs/objects.inv index 9910169d3e7d88395645611c9653edbca4e24acc..01f9e01cf01eb5a1300017d8ad782331dfda43a4 100644 GIT binary patch delta 856 zcmV-e1E>7A2EPZ8bbqy0T~E_c7{2eXXrdQ-Q3+mhXJSaa(2)2G4((xU(yz3gp?5C1%F!ksCgR1P{QSd>vnLu z>9;S@bt~Vh1Z+^qSR^%n@fYpL=G4jOXVoc!Chb{gI zCgxj`HQUq+*@z`m@dW*<=WgcX>vkEMf&omRLK~XJ!kJb|RSKuk2)bX%GyTY*8KCjUv)D{-aHEQRlN!tSZR)AVlkGvN*S74AnB zDl@?oNj4qBYAZa+P6zFgS-4beBFAwnF`!uu$+Cztp)hLXk=c3~5-bSQeN@tdrVK4% z>~}Waw0{)Ad>K=DJRAH|%vp>$94V6^t0Y4TScXdB!rr^jqq9qI(DMf06HGq*y%%=3 zc-k$r>K zG+Hb^>N7R^_(_!#ul5mx9(P@;D3BJF?0)ugvF@dB7j|cKy-4tP%+tj3VqBczJME2$ z5K*l8yUYSkYMf58OT0l9H9lclc-}>8oxnW|``ux^#aWO49URra=72+BiseagLL05E in7;%{kfWyV;0#%Q-1v*3NWs{fFs>H83V#44gU#hdWr$EbxpVWwLftUBI$}A>(RW(;P9Ygg=V1FUMT(ykB6J=2lkGk<- zC+gmWqfU8N9THe683WE21CqP_aO+!i_$z8(mgiVEjXFju+^ATWgy$Jo(ghHH-UZL- zVtFRhA=s^yBSq%wi7z+D!<}+{m(&neJ&2)=2*TCMnJ48Y6>j|*54-WfFg&^oJMU;a z%Xh$@Dha%bt$)nmJS)p^dlvWG@zszeaq6}g6dGBIaO$hNy&7a3M~4{0ppYeEoG1R| zn5u7?8uM>?_Clm6q}7Z+G{@H;%rWRb3b&Gb06k|bJWNIAsGd_|)1N1^o!5VDBmK7sTRHj%I1@4HxU z9>q0Zr+=$}o(=GvMpjrvF_4p$Wx+LMF{$Y)C%#~>iZG07+Quw+k-Hu|6#k-_g~f86 zl(MY`q~7uk~JI(rhOeZ<+a9dz1GA zx>1Fht6G(FW~Acm{n6uPvHRqt9qx7&ejq9-=19&CYu?YB@%8!m`g=Auko-GJPIo~H z2jXDa?6aR6CB4+90r6RL^}2<`P^IVsni$Ir+eZ*sFyuR;SU{{f5YrxLlK BXs7@H diff --git a/docs/searchindex.js b/docs/searchindex.js index 2fe2522..e07e2f2 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["ch-matplotlib/index", "ch-matplotlib/matplotlib-coding-styles", "ch-matplotlib/matplotlib-data", "ch-matplotlib/matplotlib-figure-axes", "ch-matplotlib/matplotlib-intro", "ch-matplotlib/matplotlib-other-parameters", "ch-matplotlib/matplotlib-plot-types", "ch-numpy/index", "ch-numpy/ndarray", "ch-numpy/ndarray-slicing-index", "ch-numpy/numpy-intro", "ch-numpy/random", "ch-pandas/data-preprocessing", "ch-pandas/dataframe-groupby", "ch-pandas/dataframe-merge-concat", "ch-pandas/dataframe-pivot-melt", "ch-pandas/dataframe-slicing", "ch-pandas/index", "ch-pandas/pandas-intro", "ch-pandas/series-dataframe", "index"], "filenames": ["ch-matplotlib/index.md", "ch-matplotlib/matplotlib-coding-styles.ipynb", "ch-matplotlib/matplotlib-data.ipynb", "ch-matplotlib/matplotlib-figure-axes.ipynb", "ch-matplotlib/matplotlib-intro.ipynb", "ch-matplotlib/matplotlib-other-parameters.ipynb", "ch-matplotlib/matplotlib-plot-types.ipynb", "ch-numpy/index.md", "ch-numpy/ndarray.ipynb", "ch-numpy/ndarray-slicing-index.ipynb", "ch-numpy/numpy-intro.ipynb", "ch-numpy/random.ipynb", "ch-pandas/data-preprocessing.ipynb", "ch-pandas/dataframe-groupby.ipynb", "ch-pandas/dataframe-merge-concat.ipynb", "ch-pandas/dataframe-pivot-melt.ipynb", "ch-pandas/dataframe-slicing.ipynb", "ch-pandas/index.md", "ch-pandas/pandas-intro.md", "ch-pandas/series-dataframe.ipynb", "index.md"], "titles": ["3. 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