# 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(...
To count unique values in the Pandas DataFrame column use theSeries.unique()function along with the size attribute. Theseries.unique()function returns all unique values from a column by removing duplicate values and the size attribute returns a count of unique values in a column of DataFrame. S...
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...
# 进行字符串分割 temp_list = [i.split(",") for i in df["Genre"]] # 获取电影的分类 genre_list = np.unique([i for j in temp_list for i in j]) # 增加新的列,创建全为0的dataframe temp_df = pd.DataFrame(np.zeros([df.shape[0],genre_list.shape[0]]),columns=genre_list) 2...
Series s.loc[indexer] DataFrame df.loc[row_indexer,column_indexer] 基础知识 如在上一节介绍数据结构时提到的,使用[](即__getitem__,对于熟悉在 Python 中实现类行为的人)进行索引的主要功能是选择较低维度的切片。以下表格显示了使用[]索引pandas 对象时的返回类型值: 对象类型 选择 返回值类型 Series seri...
函数签名: DataFrame.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', limit_area=None, downcast=None, **kwargs) 参数解释: method:插值方法,默认为linear。可选的方法包括linear,time,index,values,nearest,zero,slope,pchip,cubic, akima,barycentric等; axis:...
print(df['key_column'].nunique()) # 检测潜在的重复值 处理缺失值: df.fillna('N/A', inplace=True) # 防止因缺失值导致的合并不完整 优化内存使用:在处理大型数据集前调整数据类型: df['column'] =df['column'].astype('int32') # 将64位数据类型降为32位 ...
sort_values(ascending=False).head(10) Out[83]: INSTNM Dewey University-Manati 1.0 Yeshiva and Kollel Harbotzas Torah 1.0 Mr Leon's School of Hair Design-Lewiston 1.0 Dewey University-Bayamon 1.0 ... Monteclaro Escuela de Hoteleria y Artes Culinarias 1.0 Yeshiva Shaar Hatorah 1.0 Bais ...
lines=lines[[0,1,4]]或者lines=lines[['user','check-in_time','location_id']] dataframe连续选择多列 [0:len(decoded)-1] dataframe选择最后一列 df[df.columns[-1]]或者df.ix[:,-1] dataframe行选择 >>> dates = pd.date_range('20130101', periods=6) ...
d:\program files (x86)\python35\lib\site-packages\pandas\core\frame.pyin_getitem_column(self, key)1969#get column1970ifself.columns.is_unique:-> 1971returnself._get_item_cache(key)1972 1973#duplicate columns & possible reduce dimensionalityd:\program files (x86)\python35\lib\site-packages\pa...