unique_values = df['column_name'].unique() unique()函数会返回一个numpy数组,其中包含了所有去重之后的值。如果想要将该数组转换为列表,可以使用tolist()函数。例如: unique_values_list = df['column_name'].unique().tolist() 以上内容希望能对你有所帮助。 如何取得pandas中为空的行 可以使用isnull()...
# Quick examples of getting unique values in columns# Example 1: Find unique values of a columnprint(df['Courses'].unique())print(df.Courses.unique())# Example 2: Convert to listprint(df.Courses.unique().tolist())# Example 3: Unique values with drop_duplicatesdf.Courses.drop_duplicates(...
灵活创建和管理数据集,通过自定义创建 DataFrame ,可以方便地将各种格式的数据转化为 Pandas 的数据格式,为后续分析做好准备。 高效数据清洗与预处理,利用fillna、unique等函数,能够快速处理缺失值、去重等数据清洗工作,为模型输入做好数据预处理。 数据融合整合,Pandas 合并方法让您能够方便地横向或纵向合并多个数据源,...
values.tolist())) # 构建唯一索引 df['unique'] = df['数据日期'] + ' ' + df['产品品种'] + ' ' + df['机构名称']+ ' ' + df['指标名称'] dftest = df.set_index('unique',drop=False) df.drop(columns=['unique'],inplace=True) dftest.fillna(0,inplace = True) # 填0,防止...
import pandas as pd df_data = pd.read_csv(data_file, names=col_list) 显示原始数据,df_data.head() 运行apply函数,并记录该操作耗时: for col in df_data.columns: df_data[col] = df_data.apply(lambda x: apply_md5(x[col]), axis=1) 显示结果数据,df_data.head() 2. Polars测试 Polars...
print("Unique multiple columns : "+ str(count)) # Outputs: # Unique multiple columns : 5 Get How May Times Value Occur In case you want to get the frequency of a column useSeries.value_counts(). This function returns a Series with the counts of unique values in the specified column....
To find unique values in multiple columns, we will use the pandas.unique() method. This method traverses over DataFrame columns and returns those values whose occurrence is not more than 1 or we can say that whose occurrence is 1.Syntax:pandas.unique(values) # or df['col'].unique() ...
Python program to get unique values from multiple columns in a pandas groupby # Importing pandas packageimportpandasaspd# Importing numpy packageimportnumpyasnp# Creating a dictionaryd={'A':[10,10,10,20,20,20],'B':['a','a','b','c','c','b'],'C':['b','d','d','f','e...
Notes:Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes Categorical Period Datetime with Timezone Interval
b.sort_values(by="avg", ascending=False) print(b) print("17,---") # 查唯一值 print(b.workYear.unique()) print("18,---") # 查唯一值计数 print(b.workYear.value_counts()) print("19,---") # 描述统计汇总 print(b.describe())...