line 1 ---> 1 df.rename(str.upper) File ~/work/pandas/pandas/pandas/core/frame.py:5767, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace
"B"]) In [95]: grouped.agg("sum") Out[95]: C D A B bar one 0.254161 1.511763 three 0.215897 -0.990582 two -0.077118 1.211526 foo one -0.983776 1.614581 three -0.862495
234 rows × 4 columns 8.4 编写交易策略# 根据每周五动量信号进行交易 import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 根据每周五的趋势信号生成交易策略 def weekly_momentum_strategy(df, signal_column='趋势信号'): # 策略规则:如果趋势信号大于...
df.index, df.columns # (Index(['a', 'b', 'c', 'd'], dtype='object'), # Index(['one', 'two'], dtype='object')) (2)用多维数组字典、列表字典生成 DataFrame 多维数组的长度必须相同。如果传递了索引参数,index 的长度必须与数组一致。如果没有传递索引参数,生成的结果是 range(n),n 为...
pd.concat([df,df_new], axis='columns') 12.用多个函数聚合 orders = pd.read_csv('data/chipotle.tsv', sep='\t') orders.groupby('order_id').item_price.agg(['sum','count']).head() 13.分组聚合 import pandas as pd df = pd.DataFrame({'key1':['a', 'a', 'b', 'b', 'a'...
...: columns=pd.MultiIndex.from_arrays([["A", "B", "B"], columns]), ...: ) ...: In [135]: df.sum(axis=1, level=1) Out[135]: One Two Three 0 3 3 0 1 9 6 0 Groupby: In [136]: cats = pd.Categorical( ...: [...
['a','b','c','d'],columns=['one','two']) In [45]: df Out[45]: one two a 1.40 NaN b 7.10 -4.5 c NaN NaN d 0.75 -1.3 In [46]: df.sum()#默认求每列的和 Out[46]: one 9.25 two -5.80 dtype: float64 In [47]: df.sum(axis = 1)#传入参数axis,求每行的和 Out[47...
可以根据下划线前后的数字对列名进行排序: df2 = df.select( 'id', *sorted( df.columns[1:], key=lambda c: (int(c.split('_')[0]), int(c.split('_')[1])) )) 要获得其他所需的输出,只需在上面的代码中用1交换0。 如何更改Pandas DataFrame的列名? 你还需要找出有多少列 你可以通过 len(...
columns Returns the column labels of the DataFrame combine() Compare the values in two DataFrames, and let a function decide which values to keep combine_first() Compare two DataFrames, and if the first DataFrame has a NULL value, it will be filled with the respective value from the second...
In [132]: columns = pd.Categorical(...: ["One", "One", "Two"], categories=["One", "Two", "Three"], ordered=True...: )...:In [133]: df = pd.DataFrame(...: data=[[1, 2, 3], [4, 5, 6]],...: columns=pd.MultiIndex.from_arrays([["A", "B", "B"], columns...