# load the iris dataset from GitHuburl ='https://raw.githubusercontent.com/pycaret/pycaret/master/datasets/iris.csv'df = pd.read_csv(url) # reshape the data using the melt method to create a long format datasetmelted = pd.melt(df, id_vars=['species'], var_name='measurement', value_...
In [1]: import pandas as pd In [2]: from io import StringIO In [3]: data = "col1,col2,col3\na,b,1\na,b,2\nc,d,3" In [4]: pd.read_csv(StringIO(data)) Out[4]: col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 In [5]: pd.read_csv(StringIO(data), usecols=lam...
首先,我们将直接从 url 下载数据并将其读入 pandas DataFrame。 ## Load up the data -- this will take a couple seconds url = "https://raw.githubusercontent.com/amoreira2/Lectures/main/assets/data/49_Industry_Portfolios.CSV" industret_raw = pd.read_csv(url, parse_dates=["Date"]) pandasr...
# 导入模块 import pymysql import pandas as pd import numpy as np import time # 数据库 from sqlalchemy import create_engine # 可视化 import matplotlib.pyplot as plt # 如果你的设备是配备Retina屏幕的mac,可以在jupyter notebook中,使用下面一行代码有效提高图像画质 %config InlineBackend.figure_format =...
URL, or buffer where the pickled object will be stored... versionchanged:: 1.0.0Accept URL. URL has to be of S3 or GCS.compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'If 'infer' and 'path_or_url' is path-like, then detect compression fromthe fol...
# Webpage URL url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" # Define the column names col_names = ["sepal_length_in_cm", "sepal_width_in_cm", "petal_length_in_cm", "petal_width_in_cm", "class"] # Read data from URL iris_data = pd.read_...
from pandasql import sqldf, load_meat, load_births df1 = DataFrame({'name':['ZhangFei', 'GuanYu', 'a', 'b', 'c'], 'data1':range(5)}) pysqldf = lambda sql: sqldf(sql, globals()) sql = "select * from df1 where name ='ZhangFei'" ...
其中,read_csv 函数是 Pandas 中最常用的函数之一,用于从 CSV 文件中读取数据。...读取 CSV 文件假设我们有一个名为 data.csv 的文件,我们可以使用以下代码读取该文件:df = pd.read_csv('data.csv')print(df.head()) # 打印前5行数据...解决方案:使用 chunksize 参数分块读取文件。...CSV 文件读取...
from sklearn import datasets# 加载数据集和目标data, target = datasets.load_iris(return_X_y=True, as_frame=True)# 合并数据集和目标iris = pd.concat([data, target], axis=1, sort=False)iris# 创建groupby对象iris_gb = iris.groupby('target')# 1. 创建频率表,输出每个类中数量多少iris_gb....
from pandas import DataFrame import json url = 'http://t.weather.sojson.com/api/weather/city/101030100' resp = requests.get(url) data = json.loads(resp.text)#这里的data是一个dict jsondf = DataFrame(data['cityInfo'],columns =['city','cityId','parent','updateTime'],index=[1])#实例...