1) 可以用切片或者list 来选择行列 ,参数1为行,参数2为列,例如 df.loc[:,['A','B']] df.loc['20130101',['A','B']] 3. 用 df.iloc[] 索引,如果用切片或者list表示,只能用index 4. 用条件选择rows ,之后可以再用loc iloc选择行列 https://www.bilibili.com/video/BV1Ex411L7oT?p=12 import...
2, NA, "big", 1, 2, "red", 1, NA, 12), by2 = c("wet", "dry", 99, 95, NA, "damp", 95, 99, "red", 99, NA, NA)) aggregate(x=df[, c("v1", "v2")], by=list(mydf2$by1, mydf2$by2)
In [21]: sa.a = 5 In [22]: sa Out[22]: a 5 b 2 c 3 dtype: int64 In [23]: dfa.A = list(range(len(dfa.index))) # ok if A already exists In [24]: dfa Out[24]: A B C D 2000-01-01 0 0.469112 -1.509059 -1.135632 2000-01-02 1 1.212112 0.119209 -1.044236 2000-01...
1、索引排序df.sort_index() s.sort_index()# 升序排列df.sort_index()# df也是按索引进行排序df.team.sort_index()s.sort_index(ascending=False)# 降序排列s.sort_index(inplace=True)# 排序后生效,改变原数据# 索引重新0-(n-1)排,很有用,可以得到它的排序号s...
使用Index 级别和列分组的 DataFrame 可以通过列和索引级别的组合对 DataFrame 进行分组。您可以同时指定列名和索引名,或者使用 Grouper。 让我们首先创建一个带有 MultiIndex 的 DataFrame: In [60]: arrays = [ ...: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ...: ["one...
data.to_csv(sys.stdout,index=False,columns=['a','b']) a,b1,25,69,10 data.to_csv(sys.stdout) ,a,b,c,d,message0,1,2,3,4,hello1,5,6,7,8,world2,9,10,11,12,foo 四、DataFrame和数据库 # 可以将json格式的数据传给DataFreame# 也可以数据将数据库的rows传给DataFrame ...
loc[df.reset_index().groupby(['A'])['B'].idxmax()] # Display result print("Result:\n",res) OutputPython Pandas Programs »Normalize dataframe by group How to select rows that do not start with some str in pandas?Advertisement Advertisement ...
Pandas dataframe select rows where a list-column contains any of a list of strings Order columns of a pandas dataframe according to the values in a row How to divide two columns element-wise in a pandas dataframe? How do I find the iloc of a row in pandas dataframe?
sort_index(axis=1) # 会把列按列名顺序排列 2、数值排序sort_values() df.Q1.sort_values() df.sort_values('Q4') df.sort_values(by=['team', 'name'],ascending=[True, False]) 其他方法: s.sort_values(ascending=False) # 降序 s.sort_values(inplace=True) # 修改生效 s.sort_values(na_...
You can group DataFrame rows into a list by using pandas.DataFrame.groupby() function on the column of interest, select the column you want as a