df['r'] = some_expression # add a (virtual) column that will be computed on the fly df.mean(df.x), df.mean(df.r) # calculate statistics on normal and virtual columns 可视化方法也是: df.plot(df.x, df.y, show=True); #
...=False #用来正常显示负号 保存图形 保存绘制的图片,可指定图片的分辨率、边缘的颜色等 plt.savafig('存储文件名') # 记得加后缀,jpg/png 等 显示图形 plt.show(...我们可以使用x和y关键字绘制一列与另一列。 绘图方法允许除默认线图之外的少数绘图样式。 这些方法可以作为plot()的kind关键字参数提...
groupby(column_name).mean() # 按列名分组并计算均值 df[column_name].apply(function) # 对某一列应用自定义函数 数据可视化 import matplotlib.pyplot as plt # 绘制柱状图 df[column_name].plot(kind="bar") # 绘制散点图 df.plot(x="column_name1", y="column_name2", kind="scatter"...
Object to compute the transform on.func:string,function,list,or dictionaryFunction(s)to compute the transformwith.axis:{0or'index',1or'columns'}Axis along which thefunctionis applied:*0or'index':applyfunctionto each column.*1or'columns':applyfunctionto each row.Returns---DataFrame or Series ...
pd.show_versions() INSTALLED VERSIONS --- commit: None python: 3.7.3.final.0 python-bits: 64 OS: Darwin OS-release: 18.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 ...
source=ColumnDataSource(df), height=300, # 创建散点图: p_scatter = df.plot_bokeh.scatter( x="petal length(cm)", y="sepal width(cm)", category="species", title="Iris数据可视化", show_figure=False, # Combine Table and Scatterplot via grid layout: ...
plt.show(df.plot(kind = 'box')) 现在,我们可以用pandas模块中集成R的ggplot主题来美化图表。要使用ggplot,我们只需要在上述代码中多加一行, import matplotlib.pyplot as plt pd.options.display.mpl_style = 'default' # Sets the plotting display theme to ggplot2 ...
[currently: None]display.colheader_justify : 'left'/'right'Controls the justification of column headers. used by DataFrameFormatter.[default: right] [currently: right]display.date_dayfirst : booleanWhen True, prints and parses dates with the day first, eg 20/01/2005[default: False] [...
'2019'],'Inflation Rate':['4.47','5','No data','4.1']}# create a dataframedf=pd.DataFrame(Data)# converting each value of column to a stringdf['Inflation Rate']=pd.to_numeric(df['Inflation Rate'],errors='coerce')# show the dataframeprint(df)# show the data typesprint(df.dtypes...
In [24]: df = pd.DataFrame(data, dtype=decimal_type) In [25]: df Out[25]:0103.19<NA>1<NA> -1.23 如果您已经有一个pyarrow.Array或pyarrow.ChunkedArray,您可以将其传入arrays.ArrowExtensionArray以构造相关的Series、Index或DataFrame对象。