我有一個非常大的 100K x 100K 矩陣，為了更好地管理記憶體，我將其設定為一個資料幀，資料型別為 int8（每個單元格 1 個位元組）。但是，它被設定為每個單元格 8 個位元組的浮點數。我哪里錯了？
df = pd.DataFrame()
df=df.astype('int8')
mat_len=100,000
for i in range(0, mat_len):
new_row = pd.Series([0] * mat_len)
df = df.append(new_row, ignore_index='True') #initializing matrix
for i in range(0, mat_len):
for j in range(i,mat_len):
df.iloc[i,j] = i j #simplified calc for testing purposes
print(df.info())
輸出：資料型別：float64(100)
uj5u.com熱心網友回復：
您應該始終避免append使用 DataFrames/Series，尤其是避免在回圈中使用它。它非常非常緩慢。首先，生成資料，然后用它創建一個 DataFrame。
df.iloc[i,j] = i j #用于測驗目的的簡化計算
你的計算有多復雜？在這種簡單的情況下，您的代碼可以通過使用numpy.fromfunction
和numpy.triu
mat_len = 100_000
# create matrix from a function of the indicies
mat = np.fromfunction(lambda i,j: i j, shape=(mat_len, mat_len), dtype='int8')
# make it an upper triangular matrix
mat = np.triu(mat)
# create a DataFrame with it
df = pd.DataFrame(mat)
uj5u.com熱心網友回復：
您需要在回圈內移動轉換：
df = pd.DataFrame()
mat_len=100
for i in range(0, mat_len):
new_row = pd.Series([0] * mat_len)
df = df.append(new_row, ignore_index='True')
df=df.astype('int8')#initializing matrix
for i in range(0, mat_len):
for j in range(i,mat_len):
df.iloc[i,j] = i j #simplified calc for testing purposes
print(df.info())
回傳：
class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 100 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 100 non-null int8
1 1 100 non-null int8
2 2 100 non-null int8
3 3 100 non-null int8
4 4 100 non-null int8
5 5 100 non-null int8
6 6 100 non-null int8
7 7 100 non-null int8
8 8 100 non-null int8
9 9 100 non-null int8
10 10 100 non-null int8
11 11 100 non-null int8
12 12 100 non-null int8
13 13 100 non-null int8
14 14 100 non-null int8
15 15 100 non-null int8
16 16 100 non-null int8
17 17 100 non-null int8
18 18 100 non-null int8
19 19 100 non-null int8
20 20 100 non-null int8
21 21 100 non-null int8
22 22 100 non-null int8
23 23 100 non-null int8
24 24 100 non-null int8
25 25 100 non-null int8
26 26 100 non-null int8
27 27 100 non-null int8
28 28 100 non-null int8
29 29 100 non-null int8
30 30 100 non-null int8
31 31 100 non-null int8
32 32 100 non-null int8
33 33 100 non-null int8
34 34 100 non-null int8
35 35 100 non-null int8
36 36 100 non-null int8
37 37 100 non-null int8
38 38 100 non-null int8
39 39 100 non-null int8
40 40 100 non-null int8
41 41 100 non-null int8
42 42 100 non-null int8
43 43 100 non-null int8
44 44 100 non-null int8
45 45 100 non-null int8
46 46 100 non-null int8
47 47 100 non-null int8
48 48 100 non-null int8
49 49 100 non-null int8
50 50 100 non-null int8
51 51 100 non-null int8
52 52 100 non-null int8
53 53 100 non-null int8
54 54 100 non-null int8
55 55 100 non-null int8
56 56 100 non-null int8
57 57 100 non-null int8
58 58 100 non-null int8
59 59 100 non-null int8
60 60 100 non-null int8
61 61 100 non-null int8
62 62 100 non-null int8
63 63 100 non-null int8
64 64 100 non-null int8
65 65 100 non-null int8
66 66 100 non-null int8
67 67 100 non-null int8
68 68 100 non-null int8
69 69 100 non-null int8
70 70 100 non-null int8
71 71 100 non-null int8
72 72 100 non-null int8
73 73 100 non-null int8
74 74 100 non-null int8
75 75 100 non-null int8
76 76 100 non-null int8
77 77 100 non-null int8
78 78 100 non-null int8
79 79 100 non-null int8
80 80 100 non-null int8
81 81 100 non-null int8
82 82 100 non-null int8
83 83 100 non-null int8
84 84 100 non-null int8
85 85 100 non-null int8
86 86 100 non-null int8
87 87 100 non-null int8
88 88 100 non-null int8
89 89 100 non-null int8
90 90 100 non-null int8
91 91 100 non-null int8
92 92 100 non-null int8
93 93 100 non-null int8
94 94 100 non-null int8
95 95 100 non-null int8
96 96 100 non-null int8
97 97 100 non-null int8
98 98 100 non-null int8
99 99 100 non-null int8
dtypes: int8(100)
memory usage: 9.9 KB
None
?
uj5u.com熱心網友回復：
不要把事情復雜化，使用numpy：
df = pd.DataFrame(np.zeros((100,100), dtype='int8'))
>>> df.dtypes
0 int8
1 int8
2 int8
3 int8
4 int8
...
95 int8
96 int8
97 int8
98 int8
99 int8
Length: 100, dtype: object
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