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'] ==
unique()) ['东莞' '深圳' '广州' '北京' '上海' '南京'] 六、查看数据表数值 import pandas as pd df = pd.DataFrame(pd.read_excel('test.xlsx', engine='openpyxl')) print(df.values) [[1001 Timestamp('2024-01-02 00:00:00') '东莞' '100-A' 23 1200.0] [1002 Timestamp('2024-01...
sales.groupby("store",as_index=False).agg(unique_values=("product_code","unique")) output 15、唯一值的数量 还可以使用nunique函数找到每组中唯一值的数量。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 sales.groupby("store",as_index=False).agg(number_of_unique_values=("product_code","...
'two', 'one', 'six'], ...: 'c': np.arange(7)}) ...: # This will show the SettingWithCopyWarning # but the frame values will be set In [383]: dfb['c'][dfb['a'].str.startswith('o')] = 42 然而,这
DataFrame.pivot_table(self, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)pivot_tabel对数据格式要求不高,而且支持aggfunc/fillvalue等参数,所以应用更加广泛。 pivot_table函数的参数有values(单元格值)、index(索...
values on the otheraxes are still respected in the join.keys : sequence, default NoneIf multiple levels passed, should contain tuples. Constructhierarchical index using the passed keys as the outermost level.levels : list of sequences, default NoneSpecific levels (unique values) to use for ...
In [ ] # 运行以下代码 # creates a new column 'date' and gets the values from the index data['date'] = data.index # creates a column for each value from date data['month'] = data['date'].apply(lambda date: date.month) data['year'] = data['date'].apply(lambda date: date.ye...
pd.show_versions() 1. INSTALLED VERSIONS --- commit: None python: 3.7.3.final.0 python-bits: 64 OS: Darwin OS-release: 18.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 ...
df.sort_values("popularity",inplace=True, ascending=False)# 取某列最大值所在行df[df['popularity'] == df['popularity'].max()]# 取某列最大num行df.nlargest(num,'col_name')# 最大num列画横向柱形图df.nlargest(10).plot(kind='barh') 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ...
pandas 使用groupby(column)[“id”].nunique并尝试使用transform添加列时获得不同的值解同Series.map...