def picture_radar(file_path):
"""
:param file_path:文件路径：str
"""
birth_hcs = pd.read_csv(file_path)
# 使用loc 进行切片， 行：全选 列：选择不连续的用列表包含列名
birth_nien_hcs = birth_hcs.loc[:, [
'INFANT_ALIVE_AT_REPORT', 'MOTHER_AGE_YEARS', 'CIG_1_TRI',
'MOTHER_PRE_WEIGHT', 'OBSTETRIC_GESTATION_WEEKS', 'INFANT_WEIGHT_GRAMS'
]]
birth_nien_hcs = nien_to_nan(birth_nien_hcs, [
'MOTHER_AGE_YEARS', 'CIG_1_TRI', 'MOTHER_PRE_WEIGHT',
'OBSTETRIC_GESTATION_WEEKS', 'INFANT_WEIGHT_GRAMS'
])
# birth_nien['INFANT_ALIVE_AT_REPORT'] == 'Y' 判断row的值：等于则为True 再使用DataFrame取为True的行
y_hcs = birth_nien_hcs[birth_nien_hcs['INFANT_ALIVE_AT_REPORT'] == 'Y']
n_hcs = birth_nien_hcs[birth_nien_hcs['INFANT_ALIVE_AT_REPORT'] == 'N']
# 按列求平均值，跳过nan，
# round(decimals=2)： 四舍五入取两位小数
y_mean_hcs = list(y_hcs.mean(skipna=True, axis=0).round(decimals=2))
n_mean_hcs = list(n_hcs.mean(skipna=True, axis=0).round(decimals=2))
print('存活婴儿数据雷达图:', y_mean_hcs, '\n', '死亡婴儿数据雷达图:', n_mean_hcs)
schema_hcs = [{
'name': "母亲平均年龄",
'max': 40
}, {
'name': "母亲平均吸烟数量",
'max': 1.2
}, {
'name': "母亲平均体重",
'max': 200
}, {
'name': "怀孕平均周数",
'max': 40
}, {
'name': "婴儿平均体重",
'max': 3500
}]
radar_hcs = Radar('存活婴儿与死亡婴儿数据对比——雷达图(黄彩思)', height=500)
radar_hcs.set_radar_component(c_schema=schema_hcs)
radar_hcs.add('存活婴儿', [y_mean_hcs],
item_color='#2525f5',
symbol=None,
area_color='#2525f5',
area_opacity=0.3,
legend_top='bottom',
line_width=3)
radar_hcs.add('死亡婴儿', [n_mean_hcs],
item_color="#f9713c",
symbol=None,
area_color="#ea3a2e",
area_opacity=0.3,
legend_top='bottom',
line_width=3,
legend_text_size=20)
radar_hcs.render('radar.html')
def test_radar_user_define_schema_single():
radar = Radar("雷达图示例")
radar.set_radar_component(c_schema=c_schema, shape="circle")
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add(
"上海",
value_sh,
item_color="#b3e4a1",
symbol=None,
legend_selectedmode="single",
)
html_content = radar._repr_html_()
assert "single" in html_content
assert "multiple" not in html_content
def radar_map():
kwargs = dict(label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right',
legend_top='center',
legend_text_size=18,
label_text_size=18,
label_emphasis_textsize=18,
xaxis_name_size=18,
xaxis_label_textsize=18,
xaxis_margin=5,
xaxis_line_width=2,
yaxis_name_size=18,
yaxis_label_textsize=18,
yaxis_line_width=2,
is_splitline_show=False,
is_xaxis_boundarygap=True,
is_toolbox_show=False,
line_width=3)
timeline = Timeline(timeline_bottom=0,
width=1400,
height=700,
timeline_symbol_size=10)
for year in value_base.current_years:
radar_map = Radar('雷达图',
title_pos='center',
title_text_size=30,
title_top=10)
for area in value_base.current_areas:
data = [[]]
for index in value_base.current_indexs:
for veti_data in value_base.veti_data_dict[year]:
if veti_data[0] == area:
data[0].append(
round(veti_data[value_base.index_num_dict[index]],
2))
radar_map.set_radar_component(schema=value_base.radar_schema)
radar_map.add(area, data, **kwargs)
timeline.add(radar_map, year)
result = str(value_base.output_dir + u"雷达图.html")
timeline.render(result)
def test_radar_default_schema():
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.set_radar_component(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
radar.render()