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=
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...
2), columns=list("AB")) In [538]: st = pd.HDFStore("appends.h5", mode="w") In [539]: st.append("df", df_1, data_columns=["B"], index=False) In [540]: st.append("df", df_2, data_columns=["B"], index=False)...
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...
To select row by max value in group, we will simply groupby the columns and use theidxmax()method this method returns the index labels. Let us understand with the help of an example Python program to select row by max value in group ...
# create a dataframedframe = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['India', 'USA', 'China', 'Russia'])#compute a formatted string from each floating point value in framechangefn = lambda x: '%.2f' % x# Make changes element-wisedframe['d'].map(...
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
# create a dataframedframe = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['India', 'USA', 'China', 'Russia'])#compute a formatted string from each floating point value in framechangefn = lambda x: '%.2f' % x# Make...
meltlist 在R 中使用名为a的列表来将其融合成一个 data.frame 的表达式: a <- as.list(c(1:4, NA))data.frame(melt(a)) 在Python 中,这个列表将是一个元组的列表,因此DataFrame()方法将其转换为所需的数据框。 In [30]: a = list(enumerate(list(range(1, 5)) + [np.NAN]))In [31]: pd...
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_...