pd.describe(包括=[np.number]) return 0.00
pd.describe(include=[np.number]) return 0.00
我使用 df_30v.describe(include=[np.number])
为我提供数据框中变量的摘要。但是结果是数字太多
count 235629.000000 235629.000000 235629.000000 119748.000000
我怎样才能得到下面的结果。谢谢!
count 235629.00 235629.00 235629.00 119748.00
致电pd.set_option('precision', 2)
:
In [165]: pd.set_option('precision', 2)
In [167]: df = pd.DataFrame(np.random.uniform(0, 10**6, size=(100,5)))
In [168]: df.describe()
Out[168]:
0 1 2 3 4
count 100.00 100.00 100.00 100.00 100.00
mean 440786.89 526477.58 457295.14 498070.00 481541.09
std 286118.94 264010.57 312539.39 310191.95 274682.03
min 677.71 11862.05 2934.92 13031.54 11728.73
25% 244739.83 316760.73 188148.99 207720.23 222285.78
50% 411391.98 527119.36 406672.95 496606.54 476422.05
75% 637488.49 741362.83 745412.65 778365.74 701966.74
max 993927.91 990323.15 998025.25 999628.94 998598.52
或者,临时更改一段代码的精度,use a context manager:
with pd.option_context('precision', 2):
df = pd.DataFrame(np.random.uniform(0, 10**6, size=(100,5)))
print(df.describe())
我使用 df_30v.describe(include=[np.number])
为我提供数据框中变量的摘要。但是结果是数字太多
count 235629.000000 235629.000000 235629.000000 119748.000000
我怎样才能得到下面的结果。谢谢!
count 235629.00 235629.00 235629.00 119748.00
致电pd.set_option('precision', 2)
:
In [165]: pd.set_option('precision', 2)
In [167]: df = pd.DataFrame(np.random.uniform(0, 10**6, size=(100,5)))
In [168]: df.describe()
Out[168]:
0 1 2 3 4
count 100.00 100.00 100.00 100.00 100.00
mean 440786.89 526477.58 457295.14 498070.00 481541.09
std 286118.94 264010.57 312539.39 310191.95 274682.03
min 677.71 11862.05 2934.92 13031.54 11728.73
25% 244739.83 316760.73 188148.99 207720.23 222285.78
50% 411391.98 527119.36 406672.95 496606.54 476422.05
75% 637488.49 741362.83 745412.65 778365.74 701966.74
max 993927.91 990323.15 998025.25 999628.94 998598.52
或者,临时更改一段代码的精度,use a context manager:
with pd.option_context('precision', 2):
df = pd.DataFrame(np.random.uniform(0, 10**6, size=(100,5)))
print(df.describe())