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
columns=['python','java','scala'],# 列索引index=list('AbCd'))# 行索引display(df2)# 根据字典创建 - 字典中的key作为列索引,vaule 作为列数据 - 以列表的形式返回df3 = pd.DataFrame(data = {'python':np.random.randint(0,150,size=(4)),'java':np.random.randint(0,150,size=(4)),'scala...
"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
0.024580 0.024580 0.024580 two 2.0 0.024925 1.652692 ... 0.592714 1.109898 1.627081 [6 rows x 16 columns] 另一个聚合示例是计算每个组的唯一值数量。这类似于DataFrameGroupBy.value_counts()函数,不同之处在于它只计算唯一值的数量。 In [88]: ll = [['foo', 1], ['foo', 2], ['foo', 2],...
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'...
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.columns = ['col_one','col_two'] 如果你需要做的仅仅是将空格换成下划线,那么更好的办法是用str.replace()方法,这是因为你都不需要输入所有的列名: df.columns = df.columns.str.replace(' ','_') 上述三个函数的结果都一样,可以更改列名使得列名中...
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...
['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...
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...