In [19]: pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False) --- DuplicateLabelError Traceback (most recent call last) Cell In[19], line 1 ---> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=...
In [45]: s1 = pd.Series(np.random.randn(6), index=list('abcdef')) In [46]: s1 Out[46]: a 1.431256 b 1.340309 c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In [47]: s1.loc['c':] Out[47]: c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In...
print(np.add(series_custom, series_custom)) #add 对Series的value数值项,求和 # Apply sine function to each value np.sin(series_custom) #对series_custom 的Series的value求sin函数 # Return the highest value (will return a single value not a Series) print(np.max(series_custom)) #求最大值 ...
The string representation(代表) of a Series displaye interactively(交互地) show the index on the left and the value on the right.(索引显示在左边, 值在右边) Since we did not specify(指定) an index for the data, a default one consisting of the integer 0 throught N-1(where N is the l...
在重新分析时,会花费时间从每一行创建一个Series,并从索引和系列中调用__getitem__(每行三次)。这些 Python 函数调用是昂贵的,可以通过传递np.ndarray来改进。 In [12]: %prun -l4df.apply(lambdax: integrate_f_typed(x["a"], x["b"], x["N"]), axis=1)52533function calls (52515primitive call...
在分类数据上使用describe()将产生类似于string类型的Series或DataFrame的输出。 In [53]: cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])In [54]: df = pd.DataFrame({"cat": cat, "s": ["a", "c", "c", np.nan]})In [55]: df.describe()Out...
# this is also equivalent to ``df1.at['a','A']``In [61]: df1.loc['a', 'A']Out[61]: 0.13200317033032932 使用标签切片 使用切片与.loc一起使��时,如果起始和停止标签都存在于索引中,则返回两者之间(包括它们)的元素: In [62]: s = pd.Series(list('abcde'), index=[0, 3, 2...
obj=pd.Series([4,7,-5,3]) obj 1. 2. 0 4 1 7 2 -5 3 3 dtype: int64 1. 2. 3. 4. 5. The string representation(代表) of a Series displaye interactively(交互地) show the index on the left and the value on the right.(索引显示在左边, 值在右边) Since we did not specify(...
Now that you’ve got the basics down, let’s explore some more advanced uses of thereset_index()function. These techniques can help you manipulate your data in more complex ways. Dropping the Old Index Sometimes, you might want to reset the index without keeping the old index however the ...
Python program to convert pandas dataframe to a dictionary without index # Importing pandas packageimportpandasaspd# Creating a dictionaryd={'Name':['Pranit','Sudhir'],'Age':[21,31] }# Creating a DataFramedf=pd.DataFrame(d)# Display DataFrameprint("DataFrame1:\n",df,"\n")# Setting index...