equals() Returns True if two DataFrames are equal, otherwise False eval Evaluate a specified string explode() Converts each element into a row ffill() Replaces NULL values with the value from the previous row f
write out the binary feather-format for DataFrames DataFrame.to_latex([buf, columns, …]) Render an object to a tabular environment table. DataFrame.to_stata(fname[, convert_dates, …]) A class for writing Stata binary dta files from array-like objects ...
NumPy数组的整个数组使用一个dtype,而 pandas DataFrames的每列使用一个dtype。调用DataFrame.to_numpy()时,pandas会找到可以容纳 DataFrame中所有dtype的NumPy dtype。如果找到的dtype是object,DataFrame.to_numpy()将需要复制数据。 In [18]: df2.dtypes Out[18]: A float64 B datetime64[s] C float32 D in...
'Utah':5000} states = ['California','Ohio','Oregon','Texax'] obj3 = pd.Series(sdata) print(obj3) obj4 = pd.Series(sdata,index = states) # 将有索引的赋值,否则为空 print(obj4) pd.isnull(obj4) # 为空的 为True
DataFrame.to_feather(fname)write out the binary feather-format for DataFrames DataFrame.to_latex([buf, columns, …])Render an object to a tabular environment table. DataFrame.to_stata(fname[, convert_dates, …])A class for writing Stata binary dta files from array-like objects ...
Series/DataFrames的reindex() 方法可以使用另一个MultiIndex,甚至是元组的列表或数组来调用: 代码语言:javascript 代码运行次数:0 运行 复制 In [37]: s.reindex(index[:3]) Out[37]: first second bar one -0.861849 two -2.104569 baz one -0.494929 dtype: float64 In [38]: s.reindex([("foo", "...
df.groupby('sex').agg({'tip': np.max,'total_bill': np.sum})# count(distinct **)df.groupby('tip').agg({'sex': pd.Series.nunique}) as SQL中使用as修改列的别名,Pandas也支持这种修改: # first implementationdf.columns = ['total','pit','xes']# second implementationdf.rename(columns...
Many pandas tutorials provide very simple DataFrames to illustrate the concepts that they are trying to explain. This approach can be confusing since you can’t relate the data to anything concrete. So, for this tutorial, you’ll use two real-world datasets as the DataFrames to be merged:...
Groupby 在observed=False时也会显示“未使用”的类别: In [135]: cats = pd.Categorical(...: ["a", "b", "b", "b", "c", "c", "c"], categories=["a", "b", "c", "d"]...: )...:In [136]: df = pd.DataFrame({"cats": cats, "values": [1, 2, 2, 2, 3, 4, ...
以不同的文件格式导出DataFrames 将DataFrame写入CSV文件：df.to_csv(file_name)将DataFrame写入Excel文件：df.to_excel(file_name)将一个DataFrame写入一个SQL表：df.to_sql(tableName, connectionObject)将DataFrame写入JSON格式的文件：df.to_json(file_name)查看DataFrame信息 获取所有与索引、数据类型和内存相关的...