最简单的情况是只需传入`parse_dates=True`: ```py In [104]: with open("foo.csv", mode="w") as f: ...: f.write("date,A,B,C\n20090101,a,1,2\n20090102,b,3,4\n20090103,c,4,5") ...: # Use a column as an index, and parse it as dates. In [105]: df = pd.read_c...
Use column as index You should really useverify_integrity=Truebecause pandas won't warn you if the column in non-unique, which can cause really weird behaviour To set an existing column as index, useset_index(, verify_integrity=True): importpandasaspddf=pd.DataFrame({'name':['john','mar...
5155 method=method, 5156 copy=copy, 5157 level=level, 5158 fill_value=fill_value, 5159 limit=limit, 5160 tolerance=tolerance, 5161 ) File ~/work/pandas/pandas/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit...
DataFrame:每个column就是一个Series 基础属性shape,index,columns,values,dtypes,describe(),head(),tail() 统计属性Series: count(),value_counts(),前者是统计总数,后者统计各自value的总数 df.isnull() df的空值为True df.notnull() df的非空值为True 修改列名 代码语言:javascript 复制 df.rename(columns={...
drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False) 参数解析: - subset:列名或列名序列,对某些列来识别重复项,默认情况下使用所有列。 - keep:可选值有first,last,False,默认为first,确定要保留哪些重复项。 first:删除除第一次出现的重复项,即保留第一次出现的重复项。 last:...
按照index排序 默认ascending=True, inplace=False 四、数值计算和统计基础 1.常用数学、统计方法 基本参数:axis、skipna import numpy as npimport pandas as pddf = pd.DataFrame({'key1':[4,5,3,np.nan,2],'key2':[1,2,np.nan,4,5],'key3':[1,2,3,'j','k']},index = ['a','b',...
data.iloc[:,-1] # last column of data frame (id) 数据帧的最后一列(id) 可以使用.iloc索引器一起选择多个列和行。 1 2 3 4 5 # Multiple row and column selections using iloc and DataFrame 使用iloc和DataFrame选择多个行和列 data.iloc[0:5] # first five rows of dataframe 数据帧的前五行 ...
df.insert(loc,column,value) iii)根据已有的列创建新列 df['平均支付金额'] = df['支付金额']/df['支付买家数'] df.insert(3,'平均支付金额',df['支付金额']/df['支付买家数']) iv)删除列 df.drop('col1',axis=1,inplace=True) / del df['col2'] ...
pandas.DataFrame.drop_duplicates(self, subset=None, keep='first', inplace=False) 返回的DataFrame去掉了重复的行。 subset:可以是column label或sequence of labels, 其他。默认作用于所有的列。可以设置,如 df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})#整个列去重, 生...
# 自定义indicator column的名称并打印出res = pd.merge(df1, df2, on='coll', how='outer', indicator='indicator_column')print(res)依据index合并# 依据index合并# 定义数据集并打印出left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],'B': ['B0', 'B1', 'B2']}, index = ['K0', 'K1...