motorcycle_info = pd.read_csv(file_path) 怎么data cleaning
时间: 2024-04-25 13:24:41 浏览: 16
在加载数据之后,你可以使用以下方法对数据进行清洗:
1. 查看数据:使用 `.head()` 方法查看数据集的前几行,以确保数据正确加载。使用 `.info()` 方法查看数据集的基本信息,如列名、数据类型以及缺失值情况。
2. 处理缺失值:使用 `.isnull().sum()` 方法检查每列中的缺失值数量,并决定如何处理这些缺失值。你可以选择删除包含缺失值的行/列,使用 `.dropna()` 方法进行删除操作;或者使用插补方法,如均值、中位数或者基于机器学习算法的预测填充。
3. 处理异常值:使用统计方法,如计算变量的上下界或者计算离群值的标准差来检测异常值。根据具体情况,你可以选择删除异常值所在的行/列或者使用合适的替换方法进行处理。
4. 数据类型转换:根据数据的实际含义和需要,使用 `.astype()` 方法将变量转换为正确的数据类型。例如,将字符串转换为日期时间类型或将分类变量转换为数值变量。
5. 数据一致性检查:检查数据集中变量之间是否存在逻辑上的冲突或不一致。比如,检查数值范围是否符合预期,或者检查分类变量的取值是否正确。
6. 处理重复值:使用 `.duplicated()` 方法检查数据集中是否存在重复的记录,并使用 `.drop_duplicates()` 方法删除重复的行。
在进行数据清洗时,确保备份原始数据集,并在对数据进行任何更改之前进行测试和验证。此外,根据具体的数据集和分析需求,可能还需要额外的数据清洗步骤。
希望这些方法能帮助到你进行数据清洗!如果你有任何进一步的问题,请随时提问。
相关问题
CarlaLaneInvasionEvent.LANE_MARKING_BROKEN, CarlaLaneInvasionEvent.LANE_MARKING_OTHER,CarlaLaneInvasionEvent.LANE_MARKING_SOLID
, CarlaLaneInvasionEvent.LANE_MARKING_BROKEN_DASHED, CarlaLaneInvasionEvent.LANE_MARKING_DOUBLE_SOLID, CarlaLaneInvasionEvent.LANE_MARKING_CURB, CarlaLaneInvasionEvent.LANE_MARKING_GRASS, CarlaLaneInvasionEvent.LANE_MARKING_SNOW, CarlaLaneInvasionEvent.LANE_MARKING_OTHER_TEXTURE, CarlaLaneInvasionEvent.LANE_MARKING_BUMPS, CarlaLaneInvasionEvent.LANE_MARKING_ZEBRA, CarlaLaneInvasionEvent.LANE_MARKING_DIAGONAL_BROKEN, CarlaLaneInvasionEvent.LANE_MARKING_DIAGONAL_SOLID, CarlaLaneInvasionEvent.LANE_MARKING_CURB_INNER, CarlaLaneInvasionEvent.LANE_MARKING_CURB_OUTER, CarlaLaneInvasionEvent.LANE_MARKING_RAILWAY, CarlaLaneInvasionEvent.LANE_MARKING_STOP, CarlaLaneInvasionEvent.LANE_MARKING_ARROW, CarlaLaneInvasionEvent.LANE_MARKING_BIKE_LANE, CarlaLaneInvasionEvent.LANE_MARKING_RAILWAY_CROSSING, CarlaLaneInvasionEvent.LANE_MARKING_RAILWAY_STOP, CarlaLaneInvasionEvent.LANE_MARKING_FREE_SPACE, CarlaLaneInvasionEvent.LANE_MARKING_RAMP, CarlaLaneInvasionEvent.LANE_MARKING_SPEED_BUMP, CarlaLaneInvasionEvent.LANE_MARKING_TOLL_BOOTH, CarlaLaneInvasionEvent.LANE_MARKING_TRAFFIC_SIGNAL, CarlaLaneInvasionEvent.LANE_MARKING_TURN_LANE, CarlaLaneInvasionEvent.LANE_MARKING_YIELD_SIGN, CarlaLaneInvasionEvent.LANE_MARKING_NO_OVERTAKING, CarlaLaneInvasionEvent.LANE_MARKING_NO_PARKING, CarlaLaneInvasionEvent.LANE_MARKING_NO_STOPPING, CarlaLaneInvasionEvent.LANE_MARKING_NO_STANDING, CarlaLaneInvasionEvent.LANE_MARKING_PEDESTRIAN_CROSSING, CarlaLaneInvasionEvent.LANE_MARKING_SCHOOL_ZONE, CarlaLaneInvasionEvent.LANE_MARKING_TRAFFIC_ISLAND, CarlaLaneInvasionEvent.LANE_MARKING_ROUNDABOUT, CarlaLaneInvasionEvent.LANE_MARKING_MERGE_LEFT, CarlaLaneInvasionEvent.LANE_MARKING_MERGE_RIGHT, CarlaLaneInvasionEvent.LANE_MARKING_MERGE_SIDE, CarlaLaneInvasionEvent.LANE_MARKING_DIVIDER, CarlaLaneInvasionEvent.LANE_MARKING_BUS_LANE, CarlaLaneInvasionEvent.LANE_MARKING_MOTORWAY, CarlaLaneInvasionEvent.LANE_MARKING_PEDESTRIAN_LANE, CarlaLaneInvasionEvent.LANE_MARKING_SHARED_ZONE, CarlaLaneInvasionEvent.LANE_MARKING_TURNING_LANE, CarlaLaneInvasionEvent.LANE_MARKING_SPLIT_LANE, CarlaLaneInvasionEvent.LANE_MARKING_LANE_END, CarlaLaneInvasionEvent.LANE_MARKING_LANE_START, CarlaLaneInvasionEvent.LANE_MARKING_LANE_CHANGE, CarlaLaneInvasionEvent.LANE_MARKING_SIDEWALK, CarlaLaneInvasionEvent.LANE_MARKING_CROSSWALK, CarlaLaneInvasionEvent.LANE_MARKING_SIDE_CURB, CarlaLaneInvasionEvent.LANE_MARKING_TUNNEL, CarlaLaneInvasionEvent.LANE_MARKING_BRIDGE, CarlaLaneInvasionEvent.LANE_MARKING_COVERED, CarlaLaneInvasionEvent.LANE_MARKING_RAMP_UP, CarlaLaneInvasionEvent.LANE_MARKING_RAMP_DOWN, CarlaLaneInvasionEvent.LANE_MARKING_SHOULDER, CarlaLaneInvasionEvent.LANE_MARKING_SHOULDER_GRASS, CarlaLaneInvasionEvent.LANE_MARKING_SHOULDER_SNOW, CarlaLaneInvasionEvent.LANE_MARKING_SHOULDER_OTHER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_LINE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_SIGN, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_TEXT, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_ARROW, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_NUMBER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_OTHER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_ENTRY, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_EXIT, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_BORDER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_SPACE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_PERIMETER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_LOADING, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_UNLOADING, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_SERVICE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_PRIVATE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_PUBLIC, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_RESIDENTIAL, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_BUSINESS, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_VISITOR, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_STAFF, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_DISABLED, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_MOTORCYCLE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_BICYCLE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_CARPOOL, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_EMERGENCY, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_TAXI, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_TRUCK, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_TRAILER, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_BOAT, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_AIRCRAFT, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_OTHER_VEHICLE, CarlaLaneInvasionEvent.LANE_MARKING_PARKING_ZONE_OTHER_OBJECT
mask R_CNN例程
Mask R-CNN 是一种基于 Faster R-CNN 的目标检测算法,不仅可以检测出物体的位置,还可以精确地分割出物体的 mask。下面是一个 Mask R-CNN 的例程:
```python
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.config import Config
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
class InferenceConfig(Config):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
NUM_CLASSES = 1 + 80 # COCO has 80 classes
config = InferenceConfig()
config.display()
# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
'teddy bear', 'hair drier', 'toothbrush']
# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))
# Run detection
results = model.detect([image], verbose=1)
# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'])
```
在上面的例程中,我们首先导入所需的库和模块,包括 Mask R-CNN 的配置、模型、可视化等。然后,我们设置了模型的超参数和文件路径,以及加载了预训练的 COCO 权重。接下来,我们定义了一个 InferenceConfig 类,它继承自 Config 类,并设置了模型的 GPU 数量、每个 GPU 的图片数量和类别数。然后,我们在 inference 模式下创建了模型对象,并加载了预训练的 COCO 权重。最后,我们从图片文件夹中随机加载一张图片,并对其进行目标检测和分割,最后可视化检测结果。