unique()}") # Extending the idea from 1 column to multiple columns print(f"Unique Values from 3 Columns:\ {pd.concat([df['FirstName'],df['LastName'],df['Age']]).unique()}") Python Copy输出:Unique FN: [‘Arun’ ‘Navneet’ ‘Shilpa’ ‘Prateek’ ‘Pyare’] Unique Values from...
Find Unique Values in Multiple Columns In case you want to get unique values on multiple columns of DataFrame usepandas.unique()function, using this you can also get unique values of a single column. Syntax: # Syntax pandas.unique(values) Let’s see an example. Since the unique() function...
We are supposed to find the unique values from multiple groupby. Getting unique values from multiple columns in a pandas groupby For this purpose, we can use the combination ofdataframe.groupby()andapply()method with the specifiedlambda expression. Thegroupby()method is a simple but very use...
How to delete the last row of data of a pandas DataFrame? Find the column name which has the maximum value for each row How to find unique values from multiple columns in pandas? How to modify a subset of rows in a pandas DataFrame?
要执行表格级别的转换,其中整个DataFrame中的所有标签都用作每列的类别,可以通过categories = pd.unique(df.to_numpy().ravel())来以编程方式确定categories参数。 如果你已经有了codes和categories,你可以使用from_codes()构造函数,在正常构造模式下保存因子化步骤: 代码语言:javascript 复制 In [37]: splitter = ...
在筛选出ID列后，在axis=1上使用nunique：
data.shape # 行数列数data.dtypes # 所有列的数据类型data['id'].dtype # 某一列的数据类型data.ndim # 数据维度data.index # 行索引data.columns # 列索引data.values # 对象值 3.2 数据集整体情况查询 data.head() # 显示头部几行(默认5行)data.tail() # 显示末尾几行(默认5行)data.info() # ...
In [49]: s1.loc['c':] = 0 In [50]: s1 Out[50]: a 1.431256 b 1.340309 c 0.000000 d 0.000000 e 0.000000 f 0.000000 dtype: float64 使用DataFrame: 代码语言:javascript 复制 In [51]: df1 = pd.DataFrame(np.random.randn(6, 4), ...: index=list('abcdef'), ...: columns=list(...
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...
).cumcount() +1).astype() df.pivot_table(index="device_id", values=cols_of_interest, columns="C") temp_a temp_b temp_c C C_1 C_2 C_3 C_1 C_2 C_3 C_1 C_2 C_3 device_id00.20.1NaN0.80.40.9