sdata={'Joolin':20,'Jay':46}obj2=pd.Series(sdata)print(obj2) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Joolin20Jay46dtype:int64 可以看到,由于字典使用键值对的方式,那么这样直接创建可以省去了创建index的操作。 当然,你也依旧可以指定你的index,那样就
Write a Pandas program to read coalpublic2013.xlsx and then print the type of each column along with its unique value counts. Pandas Pivot Table Exercises Home ↩ Pandas Exercises Home ↩ Previous:Write a Pandas program to import given excel data (coalpublic2013.xlsx ) into a Pandas dataf...
(self) 1489 ref = self._get_cacher() 1490 if ref is not None and ref._is_mixed_type: 1491 self._check_setitem_copy(t="referent", force=True) 1492 return True -> 1493 return super()._check_is_chained_assignment_possible() ~/work/pandas/pandas/pandas/core/generic.py in ?(self) ...
s = pd.Series(data, index=index) 在这里,data可以是许多不同的东西: 一个Python 字典 一个ndarray 标量值(比如 5) 传递的索引是一个轴标签列表。因此,这根据data 是的情况分为几种情况: 来自ndarray 如果data是一个 ndarray,则索引必须与data的长度相同。如果没有传递索引,将创建一个具有值[0, ..., l...
read_csv("data.csv") 数据探索和清洗 # 查看数据集的前几行 df.head() # 查看数据集的基本信息,如列名、数据类型、缺失值等 df.info() # 处理缺失值 df.dropna() # 删除缺失值 df.fillna(value) # 填充缺失值 # 数据转换和处理 df.groupby(column_name).mean() # 按列名分组并...
print("Get type of the columns:\n", df.dtypes) Yields below output. Convert Column to Int (Integer) You can use pandasDataFrame.astype()function to convert column to int(integer). You can apply this to a specific column or to an entire DataFrame. To cast the data type to a 64-bit...
(data) In [5]: df["categorical"] = df["object"].astype("category") In [6]: df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 int64 5000 non-null int64...
-> 4133 new_data = self._mgr.take(4134 indices,4135 axis=self._get_block_manager_axis(axis),4136 verify=True,4137 )4138 return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(4139 self, method="take"4140 )File ~/work/pandas/pandas/pandas/core/internals/managers...
12.Drop Data df.drop(columns=['columnName']) Series.drop(['index']) 删除指定行 删除一个变量 13.转换数据类型 df.dtypes df['columnName'] = df['columnName'].astype('dataType') pd.melt(frame=dataFrameName,id_vars = 'columnName', value_vars= ['columnName']) 14.Apply函数 Method...
df.info()"""<class'pandas.core.frame.DataFrame'>RangeIndex:1000000entries,0to999999Datacolumns(total14columns): #ColumnNon-NullCountDtype---0CID1000000non-nullobject1Name1000000non-nullobject2Age1000000non-nullint643City1000000non-nullobject4Plate1000000non-nullobject5...