1 Batman 1940-04-25 NaN 2 Catwoman Bullwhip NaT 13.0 df.dropna(axis=1,inplace=True) df #这个修改了原数据,尽量不用 name 0 Alfred 1 Batman 2 Catwoman 6.4 Filling null values 填充空值 df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], ... "toy": [np.nan, ...
df=df.reset_index()# 重置行的索引 df.head()# 删除多列df=df.drop(["name","cabin","ticket"],axis=1)# 暂时不需要类型为文本的数据条目 df.head()# 特征值映射df['sex']=df['sex'].map({'female':0,'male':1}).astype(int)df["embarked"]=df['embarked'].dropna().map({'S':0,'...
items:Each item in this axis corresponds to one data frame, and this is calledaxis 0. major_axis:This axis actually contains the rows or indexes of each of the data frames, and this is calledaxis 1. minor_axis:This axis actually contains all the columns of each of the data frames, an...
dataset.iloc[[-1]].median(axis=1) The output of the preceding code is as follows: Figure 1.35: Usage of the median method on the last row Use reverse indexing to get the last three columns with dataset[-3:] and calculate the median for each of them: dataset[-3:].median(axis=1)...
axis=0 major_axis : Index or array-like axis=1 minor_axis : Index or array-like axis=2 dtype : dtype, default None Data type to force, otherwise infer copy : boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input Attributes at Fast label-based...
同样,如果属性与以下列表中的任何内容冲突,则该属性将不可用:index、major_axis、minor_axis、items。 在任何这些情况下,标准索引仍将起作用,例如,s['1']、s['min']和s['index']将访问相应的元素或列。 如果您正在使用 IPython 环境,还可以使用制表符补全来查看这些可访问的属性。 您还可以将dict分配给Da...
Note that by default it usesaxis=0meaning itapplies a function to each column. Key Points – applywith theaxis=1parameter allows you to apply a function to each row, enabling operations based on row data. You can pass a lambda function toapplyfor concise, on-the-fly operations without nee...
1-dimensional labeled array 2-dimensional labeled data structure Contains data of a single data type Can contain data of multiple data types across columns Single column of data Multiple columns, each can be of different data types Indexed by a single axis (labels) Indexed by two axes (rows ...
1000 rows and 11 columns. Note that the rows are at index zero of this tuple and columns are at index one of this tuple. This is why axis=1 affects columns. This comes from NumPy, and is a great example of why learning NumPy is worth your time.Imputation...
The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data. They are −items − axis 0, each item corresponds to a DataFrame contained inside. major_axis − axis 1, it is the index (rows) of each of the DataFrames. minor_axis...