matplotlib 绘图数据问题
matplotlib graphing data problems
我刚刚完成了对我正在工作的项目的预测。我想尝试使用图表进行一些可视化，但我很难找到合适的图表，我拥有的数据非常大。我将在底部留下我的代码和 1 列的结果示例。这只有 1 列，我想先绘制 1 列的图表，看看它是如何工作的。我试过使用条形图，结果有点奇怪，只有一个完整的蓝色实心条。所以我不确定哪种图表适合此类信息。
读入测试和目标数据
训练和测试必须按列匹配
火车=pd.read_csv('C:/Users/Michael/Desktop/train.csv/train.csv',parse_dates = ['Dates'])
测试=pd.read_csv('C:/Users/Michael/Desktop/test.csv/test.csv',parse_dates = ['Dates'])
# TRAINING data
#Convert crime labels to numbers
df_crime = preprocessing.LabelEncoder()
crime = df_crime.fit_transform(train.Category)
#Get binarized weekdays, districts, and hours using dummy variables
days = pd.get_dummies(train.DayOfWeek)
district = pd.get_dummies(train.PdDistrict)
hour = train.Dates.dt.hour
hour = pd.get_dummies(hour)
#Build new array
train_data = pd.concat([hour, days, district], axis=1)
train_data['crime']=crime
#train_data.head()
#Repeat for test data
days = pd.get_dummies(test.DayOfWeek)
district = pd.get_dummies(test.PdDistrict)
hour = test.Dates.dt.hour
hour = pd.get_dummies(hour)
test_data = pd.concat([hour, days, district], axis=1)
features = ['Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', 'Tuesday',
'Wednesday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',
'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN']
training, testing = train_test_split(train_data, train_size=.60)
#bernoulliNB
# predicting only on the training data
model_B = BernoulliNB()
model_B.fit(training[features], training['crime'])
predicted2 = np.array(model_B.predict_proba(testing[features]))
log_loss(testing['crime'], predicted2)
# predictingon the test data, using bernoulli model
predicted3 = model_B.predict_proba(test_data[features])
#Write results
result=pd.DataFrame(predicted3, columns=df_crime.classes_)
# this is an example of 1 of my columns that i would like to graph
result['SUICIDE']
0 0.000432
1 0.000432
2 0.000760
3 0.000903
4 0.000903
5 0.001089
6 0.000903
7 0.000903
8 0.000550
9 0.000744
10 0.000903
11 0.000550
12 0.000550
13 0.000744
14 0.000744
15 0.000219
16 0.001089
17 0.000903
18 0.000760
19 0.000760
20 0.000760
21 0.000550
22 0.000744
23 0.000903
24 0.000760
25 0.000787
26 0.000760
27 0.000265
28 0.000903
29 0.001089
你对输出的期望非常含糊，但我认为你应该查看 seaborn 包，尤其是关于 visualising univariate datasets 的教程部分，它应该会给你一些关于如何可视化输出的示例和想法。
我刚刚完成了对我正在工作的项目的预测。我想尝试使用图表进行一些可视化，但我很难找到合适的图表，我拥有的数据非常大。我将在底部留下我的代码和 1 列的结果示例。这只有 1 列，我想先绘制 1 列的图表，看看它是如何工作的。我试过使用条形图，结果有点奇怪，只有一个完整的蓝色实心条。所以我不确定哪种图表适合此类信息。
读入测试和目标数据
训练和测试必须按列匹配
火车=pd.read_csv('C:/Users/Michael/Desktop/train.csv/train.csv',parse_dates = ['Dates']) 测试=pd.read_csv('C:/Users/Michael/Desktop/test.csv/test.csv',parse_dates = ['Dates'])
# TRAINING data
#Convert crime labels to numbers
df_crime = preprocessing.LabelEncoder()
crime = df_crime.fit_transform(train.Category)
#Get binarized weekdays, districts, and hours using dummy variables
days = pd.get_dummies(train.DayOfWeek)
district = pd.get_dummies(train.PdDistrict)
hour = train.Dates.dt.hour
hour = pd.get_dummies(hour)
#Build new array
train_data = pd.concat([hour, days, district], axis=1)
train_data['crime']=crime
#train_data.head()
#Repeat for test data
days = pd.get_dummies(test.DayOfWeek)
district = pd.get_dummies(test.PdDistrict)
hour = test.Dates.dt.hour
hour = pd.get_dummies(hour)
test_data = pd.concat([hour, days, district], axis=1)
features = ['Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', 'Tuesday',
'Wednesday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',
'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN']
training, testing = train_test_split(train_data, train_size=.60)
#bernoulliNB
# predicting only on the training data
model_B = BernoulliNB()
model_B.fit(training[features], training['crime'])
predicted2 = np.array(model_B.predict_proba(testing[features]))
log_loss(testing['crime'], predicted2)
# predictingon the test data, using bernoulli model
predicted3 = model_B.predict_proba(test_data[features])
#Write results
result=pd.DataFrame(predicted3, columns=df_crime.classes_)
# this is an example of 1 of my columns that i would like to graph
result['SUICIDE']
0 0.000432
1 0.000432
2 0.000760
3 0.000903
4 0.000903
5 0.001089
6 0.000903
7 0.000903
8 0.000550
9 0.000744
10 0.000903
11 0.000550
12 0.000550
13 0.000744
14 0.000744
15 0.000219
16 0.001089
17 0.000903
18 0.000760
19 0.000760
20 0.000760
21 0.000550
22 0.000744
23 0.000903
24 0.000760
25 0.000787
26 0.000760
27 0.000265
28 0.000903
29 0.001089
你对输出的期望非常含糊，但我认为你应该查看 seaborn 包，尤其是关于 visualising univariate datasets 的教程部分，它应该会给你一些关于如何可视化输出的示例和想法。