dictionary

问题 network={1:[2,3,4],2:[1,3,4], 3:[1,2], 4:[1,3,5], 5:[6,7,8], 6:[5,8],7:[5,6], 8:[5,6,7]} str1='network.csv' output = open(str1,'w') for ii1 in network.keys(): output.write(repr(ii1)+":[") for n in network[ii1]: output.write(' %s,'%(repr(n))) output.write('\b'+']\n') output.close() What I expect is something like: 1:[ 2, 3, 4] 2:[ 1, 3, 4] 3:[ 1, 2] 4:[ 1, 3, 5] 5:[ 6, 7, 8] 6:[ 5, 8] 7:[ 5, 6] 8:[ 5, 6, 7] but what I get is: 1:[ 2, 3, 4,] 2:[ 1, 3, 4,] 3:[ 1, 2,] 4:[ 1, 3, 5,] 5:[ 6, 7, 8,] 6

问题 I want to plot my Geopandas df on a map. as a background i want a (road) map of the area. loving the OSMnx package, i'm trying to figure out how to use it's output (shapefile? network?) as my plot background import osmnx as ox G= ox.core.graph_from_place('Chengdu City', network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, name='unnamed', which_result=1, buffer_dist=None, timeout=180, memory=None, max_query_area_size=2500000000, clean_periphery=True,

问题 I'm getting a data set that's formatted as a list of key-value pairs. The key is the data source, and the value is the data element. For example: [('a', 3), ('b', 5), ('a', 7), ('c', 15), ('d', 12)] I want to turn this list into a dictionary. I could use Python's built-in dict() , but it throws away redundant values and keeps only the last value associated with a given key. I would like redundant values to go into a list, as follows: {'a': [3, 7], 'b': [5], 'c': [15], 'd': [12]} Is there a

问题 In Django, what is the most efficient way to create a nested dictionary of data from querying related and child models? For example, if I have the following models: Parent Children Pets I've seen django's model_to_dict method, and that's pretty cool, so I imagine I could loop through each level's queryset and create a bunch of DB calls on each level, for each instance, but is there a better way? For example, could "prefetch_related" be used to get all three tiers as it is used to get two

问题 So i have following dict: my_dict{'key1': 'value1', 'key2': 'value2', 'key3': json.dumps([ {"**subkey1**": "subvalue1", "**subkey2**": "subvalue2",}, {"**subkey1**": "other_subvalue", "**subkey2**":"other_subvalue2"}]) } What I need is to somehow made a def where i have to check and for each subkey2 to change its value only for the def itself And all subkey1 to check if its value is the same like the second subkey1 Please note I am talking about only subkey1 which I have twice. I don't want

问题 I have a dict of lists say: data = {'a': [80, 130], 'b': [64], 'c': [58,80]} How do I flatten it and convert it into dataframe like the one below: 回答1: Use nested list comprehension with if-else if want no count one element lists: df = pd.DataFrame([('{}{}'.format(k, i), v1) if len(v) > 1 else (k, v1) for k, v in data.items() for i, v1 in enumerate(v, 1)], columns=['Index','Data']) print (df) Index Data 0 a1 80 1 a2 130 2 b 64 3 c1 58 4 c2 80 EDIT: data = {'a': [80, 130], 'b': np.nan, 'c':

问题 I have several dictionaries set up as follows: Dict1 = {'Orange': ['1', '2', '3', '4']} Dict2 = {'Red': ['3', '4', '5']} And I'd like the output to be one combined dataframe: | Type | Value | |--------------| |Orange| 1 | |Orange| 2 | |Orange| 3 | |Orange| 4 | | Red | 3 | | Red | 4 | | Red | 5 | I tried splitting everything out but I only get Dict2 in this dataframe. mydicts = [Dict1, Dict2] for x in mydicts: for k, v in x.items(): df = pd.DataFrame(v) df['Type'] = k 回答1: One option is using

问题 Hi I have three lists and I want to create a three-level nested dictionary using one line. i.e., l1 = ['a','b'] l2 = ['1', '2', '3'] l3 = ['d','e'] I'd want to create the following nested dictionary: nd = {'a':{'1':{'d':0},{'e':0},'2':{'d':0},{'e':0},'3':{'d':0},{'e':0},'b':'a':{'1':{'d':0},{'e':0},'2':{'d':0},{'e':0},'3':{'d':0},{'e':0}} I tried using zip to do the outer loop and add the lists but elements get replaced. I.e., this does not work: nd = {i:{j:{k:[]}} for i in zip(l1,l2,l3)} 回答1

问题 I'm trying to create a pandas dataframe from a dictionary. The dictionary is set up as nvalues = {"y1": [1, 2, 3, 4], "y2": [5, 6, 7, 8], "y3": [a, b, c, d]} I would like the dataframe to include only "y1" and " y2" . So far I can accomplish this using df = pd.DataFrame.from_dict(nvalues) df.drop("y3", axis=1, inplace=True) I would like to know if it is possible to accomplish this without having df.drop() 回答1: You can specify columns in the DataFrame constructor: pd.DataFrame(nvalues, columns

问题 I'm trying to create a pandas dataframe from a dictionary. The dictionary is set up as nvalues = {"y1": [1, 2, 3, 4], "y2": [5, 6, 7, 8], "y3": [a, b, c, d]} I would like the dataframe to include only "y1" and " y2" . So far I can accomplish this using df = pd.DataFrame.from_dict(nvalues) df.drop("y3", axis=1, inplace=True) I would like to know if it is possible to accomplish this without having df.drop() 回答1: You can specify columns in the DataFrame constructor: pd.DataFrame(nvalues, columns