Data Visualization-Python

Prerequisites:

Familiarity with cognitive new programming tools (Jupyter Notebook)

1, installation: The use of PIP to install Jupyter. Enter the installation command PIP install Jupyter can be;

2, start: After the installation is complete, we can find Jupyter-notebook This application in the following directory; double-click Start

As shown in the following:

3. Open the browser compiler

The programming tool is ready to complete.

Practical Data Visualization Tutorial:

Import Pymongo Import Charts

 client = Pymongo. Mongoclient ( '  localhost  " , 27017) Ceshi  = Client[ ceshi   "  ]item_info  = Ceshi[ " item_info  Span style= "COLOR: #800000" > ' ] 

 for  i in  item_info.find (). Limit (300< Span style= "color: #000000"):  if  i[  area   ' ] = = [ " ":  pass  else  :  print  (I["  area   ' ]) 

 area_list = []  for  i in  item_info.find (). Limit (300 if  i[ " area   "] = = ["   ]:  pass  else  : Area_list.append (i[ "  area   "][1]) Area_index  = list (set (area_list))  print  (Area_index) 

Post_times = [] for in area_index:    post_times.append (Area_list.count (index))  Print(post_times)    

Data_gen ('column')

defData_gen (types): Length=0ifLength <=Len (area_index): forArea,timesinchZip (area_index,post_times): Data= {                'name': Area,'Data': [Times],'type': Types}yieldData Length+ = 1

 for  in Data_gen ('column'):    print(i)

 for  in Data_gen ('column')]charts.plot (series,show=' ) inline ', Options=dict (title=dict (text=' Hangzhou Post Data statistics- Wang ')))

Final Run Result:

Summarize the points of knowledge:

1, the introduction and use of charts module;

2, the list of append () function use;

3, COUNT () function use;

4, the use of Set function sets ();

5, the use of list analytic type;

6, the use of the zip () function;

7, the use of yield generator;

8, MongoDB database operation use;

Data Visualization-Python