import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') # 插入列 df.insert(loc=2, column='爱好', value=None) # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=False) test() 3、插入多列 假设我需要在D列(班级)后面插入5列,表头名...
而在Pandas中,我们可以通过 df.style.bar()来进行数据条绘制 Signature:df.style.bar( subset: 'Subset | None' = None, axis: 'Axis | None' = 0, color='#d65f5f', width: 'float' = 100, align: 'str' = 'left', vmin: 'float | None' = None, vmax: 'float | None' = None,) ->...
# 计算 RFM 分数 def calculate_rfm(df): # Recency 分数(越小越好) df['R_Score'] = pd.qcut(df['Last_Login_Days_Ago'], q=5, labels=[5, 4, 3, 2, 1]) # Frequency 分数(越高越好) df['F_Score'] = pd.qcut(df['Purchase_Frequency'], q=5, labels=[1, 2, 3, 4, 5]) # ...
df.iloc[:, where] 下标区间的列(integer) df.iloc[where_i, where_j] indtege行列索引 df.at[label_i, label_j] 通过行列的label来取值 df.iat[i, j] 行列位置来选取 reindex method Select either rows or columns by labels get_value, setvalue methods Select single value by row and column la...
2 C++ 30 50'''#获取数据方式一:使用列索引,实现数据获取某一行数据 df[列名]等于df.列名print(f'通过df1.name方式获取\n{df1.name}')'''通过df1.name方式获取 0 java 1 python 2 C++ Name: name, dtype: object'''print(f'通过df1["name"]方式获取\n{df1["name"]}')'''通过df1["name"]方...
.get_item() File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'a' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[27], line 1 ---> 1 df.apply(...
df[df[column_name].duplicated()] # 查看column_name字段数据重复的数据信息 4.数据选取 常用的数据选取的10个用法: df[col] # 选择某一列 df[[col1,col2]] # 选择多列 s.iloc[0] # 通过位置选取数据 s.loc['index_one'] # 按索引选取数据 df.iloc[0,:] # 返回第 df.iloc[0,0] # 返回第...
print('Row count is:', len(df.axes[0])) # Outputs: # Row count is:5 4. Using df.shape[0] to Get Rows Count PandasDataFrame.shapereturns the count of rows and columns,df.shape[0]is used to get the number of rows. Usedf.shape[1]to get the column count. ...
df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 6040 entries, 0 to 6039 Data columns (total 5 columns): UserID 6040 non-null int64 Gender 6040 non-null object Age 6040 non-null int64 Occupation 6040 non-null int64 Zip-code 6040 non-null object dtypes: int64(3), object(2...
print(customers_df['customer_id'].is_unique) # 理想情况下应返回True 2、左连接:保留主表完整性的操作 应用场景:需要保留左侧DataFrame的所有记录,即使部分记录在右侧表中没有匹配项(例如,保留所有客户记录,包括无订单的客户)。 left_merged=pd.merge(customers_df, orders_df,on='customer_id',how='left'...