Pythonフレーズ2
順位 | 名前 | スコア | 称号 | 打鍵/秒 | 正誤率 | 時間(秒) | 打鍵数 | ミス | 問題 | 日付 |
---|---|---|---|---|---|---|---|---|---|---|
1 | ku | 4158 | C | 4.1 | 99.4% | 120.0 | 502 | 3 | 29 | 2024/10/11 |
2 | ss | 2500 | E | 2.7 | 90.7% | 120.0 | 334 | 34 | 19 | 2024/09/02 |
3 | noa | 2200 | F+ | 2.2 | 100% | 120.0 | 264 | 0 | 17 | 2024/09/14 |
4 | ACCO | 2133 | F+ | 2.2 | 95.0% | 120.0 | 270 | 14 | 10 | 2024/09/02 |
関連タイピング
-
プレイ回数3934英語長文120秒
-
プレイ回数208英語長文220打
-
プレイ回数599英語長文60秒
-
プレイ回数304長文120秒
-
プレイ回数3359短文英字90秒
-
プレイ回数57357打
-
プレイ回数482短文9打
-
プレイ回数1442短文英字429打
問題文
(df.dropna())
df.dropna()
(df.fillna(0))
df.fillna(0)
(df.fillna(method='ffill'))
df.fillna(method='ffill')
(de.fillna(df.mean()))
de.fillna(df.mean())
(.median())
.median()
(.std())
.std()
(df.describe())
df.describe()
(df.corr())
df.corr()
(%matplotlib inline)
%matplotlib inline
(import matplotlib.pyplot as plt)
import matplotlib.pyplot as plt
(plt.plot([]))
plt.plot([])
(plt.show())
plt.show()
(marker='')
marker=''
(color='')
color=''
(linestyle='')
linestyle=''
(linewidth='')
linewidth=''
(label='')
label=''
(plt.xlabel(''))
plt.xlabel('')
(plt.title(''))
plt.title('')
(plt.legend())
plt.legend()
(plt.scatter())
plt.scatter()
(plt.imshow(img,''))
plt.imshow(img,'')
(re.sub())
re.sub()
([^a])
[^a]
(a+)
a+
($$a_1$$)
$$a_1$$
($$x^2$$)
$$x^2$$
($$\sqrt x$$)
$$\sqrt x$$
($$\sin x$$)
$$\sin x$$
($$\frac{a}{b}$$)
$$\frac{a}{b}$$
($$\sum_{k=1}^n a_k$$)
$$\sum_{k=1}^n a_k$$
($$\prod_{k=1}^n a_k$$)
$$\prod_{k=1}^n a_k$$
(plt.hist())
plt.hist()
(.strip())
.strip()
(.keys())
.keys()
(.values())
.values()
(.items())
.items()
(print('{0},{1},{0}'.format('Hello','world')))
print('{0},{1},{0}'.format('Hello','world'))
(input(''))
input('')
(*args)
*args
(**kwargs)
**kwargs
(___doc___)
___doc___
(lambda)
lambda
(yield)
yield
(if __name__=='__main__')
if __name__=='__main__'
(__del__():)
__del__():
(self)
self
(readlines())
readlines()
(os.rename())
os.rename()
(os.mkdir)
os.mkdir
(os.rmdir)
os.rmdir
(os.remove)
os.remove
(X.T)
X.T
(np.linalg.inv())
np.linalg.inv()
(from sklearn.linear_model import LinearRegression)
from sklearn.linear_model import LinearRegression
(model=LinearRegression)
model=LinearRegression
(model.fit())
model.fit()
(model.coef_)
model.coef_
(model.score())
model.score()
(model.predict())
model.predict()
(import seaborn as sns)
import seaborn as sns
(sns.distplot())
sns.distplot()
(sns.pairplot())
sns.pairplot()
(from sklearn.model_selection import train_test_split)
from sklearn.model_selection import train_test_split
(from sklearn.externals import joblib)
from sklearn.externals import joblib
(joblib.dump())
joblib.dump()
(joblib.load())
joblib.load()
(np.set_printoptions(precision=3,suppress=True))
np.set_printoptions(precision=3,suppress=True)
(high=mean[col]-3*sigma[col])
high=mean[col]-3*sigma[col]
(df[(df[col]>low)&(df[col]<high)])
df[(df[col]>low)&(df[col]<high)]
(import chainer.links as L)
import chainer.links as L
(fc=L.Linear())
fc=L.Linear()
(import chainer.functions as F)
import chainer.functions as F
(F.relu())
F.relu()
(F.mean_squared_erro())
F.mean_squared_erro()
(x.astype('f'))
x.astype('f')
(super().__init__())
super().__init__()
(with self.init_scope():)
with self.init_scope():
(list(zip()))
list(zip())
(model=L.Classifier())
model=L.Classifier()
(chainer.datasets.split_dataset_random())
chainer.datasets.split_dataset_random()
(chainer.optimizers.SGD())
chainer.optimizers.SGD()
(optimizer.setup(model))
optimizer.setup(model)
(L.BatchNormalization())
L.BatchNormalization()
(chainer.iterators.SerialIterator())
chainer.iterators.SerialIterator()
(from chainer.training import extensions)
from chainer.training import extensions