计算pandas Groupby对象中唯一值的最简单方法是使用nunique()方法。该方法返回Groupby对象中每个组的唯一值的数量。 考虑下面的代码示例。 示例 importpandasaspd# Load sample datadf=pd.read_csv('data.csv')# Group data by column 'A' and count unique val
In the above example, thenunique()function returns a pandas Series with counts of distinct values in each column. Note that, for theDepartmentcolumn we only have two distinct values as thenunique()function, by default, ignores all NaN values. 2. Count of unique values in each row You can ...
包含values、index、columns、ndim和shape。 Pandas索引操作 1.重建索引
将date变量,转化为 pandas 中的 datetine 变量 df.info()<class'pandas.core.frame.DataFrame'>RangeIndex:360entries,0to359Datacolumns(total5columns):# Column Non-Null Count Dtype---0id360non-nullint641date360non-nulldatetime64[ns]2产品360non-nullobject3销售额360non-nullfloat644折扣360non-nullfl...
(1)‘split’ : dict like {index -> [index], columns -> [columns], data -> [values]} split 将索引总结到索引,列名到列名,数据到数据。将三部分都分开了 (2)‘records’ : list like [{column -> value}, … , {column -> value}] records 以columns:values的形式输出 (3)‘index’ : dic...
Series(["S", "S", None, "M", "L", "S", None, "XL", "S", "M",]) # Get count of each value, it does not count missing values size.value_counts() 代码语言:python 代码运行次数:0 运行 AI代码解释 # pass dropna=False to get missing value count size.value_counts(dropna=False...
以下是一些示例用法：对 Series 使用 nunique：import pandas as pddata = pd.Series([1, 2, 2, 3, 4, 4, 4, 5, 5, None])# 计算 Series 中的唯一值数量unique_count = data.nunique()print(unique_count)输出：5在这个示例中，nunique 函数计算了 Series 中的唯一值数量，忽略了缺失值（None），...
Python program to count by unique pair of columns in pandas# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d = { 'id': ['192', '192', '168', '168'], 'user': ['a', 'a', 'b', 'b'] } # Creating a ...
(2) unique和nunique data['column'].nunique():显示有多少个唯一值 data['column'].unique():显示所有的唯一值 (3) count和value_counts data['column'].count():返回非缺失值元素个数 data['column'].value_counts():返回每个元素有多少个
import numpy as np import matplotlib.path as mpath # 数据准备 species = df['species'].unique() data = [] # 只选择数值列(排除 species 列) numeric_columns = df.columns[:-1] for s in species: data.append(df[df['species'] == s][numeric_columns].mean().values) # 将 data 列表转换...