Pandas之DataFrame——Part 1
''' 【课程2.5】 Pandas数据结构Dataframe：基本概念及创建 "二维数组"Dataframe：是一个表格型的数据结构，包含一组有序的列，其列的值类型可以是数值、字符串、布尔值等。 Dataframe中的数据以一个或多个二维块存放，不是列表、字典或一维数组结构。 '''
# Dataframe 数据结构 # Dataframe是一个表格型的数据结构，“带有标签的二维数组”。 # Dataframe带有index（行标签）和columns（列标签） data = {'name':['Jack','Tom','Mary'], 'age':[18,19,20], 'gender':['m','m','w']} frame = pd.DataFrame(data) print(frame) print(type(frame)) print(frame.index,'\n该数据类型为：',type(frame.index)) print(frame.columns,'\n该数据类型为：',type(frame.columns)) print(frame.values,'\n该数据类型为：',type(frame.values)) # 查看数据，数据类型为dataframe # .index查看行标签 # .columns查看列标签 # .values查看值，数据类型为ndarray
输出：
age gender name 0 18 m Jack 1 19 m Tom 2 20 w Mary <class 'pandas.core.frame.DataFrame'> RangeIndex(start=0, stop=3, step=1) 该数据类型为： <class 'pandas.indexes.range.RangeIndex'> Index(['age', 'gender', 'name'], dtype='object') 该数据类型为： <class 'pandas.indexes.base.Index'> [[18 'm' 'Jack'] [19 'm' 'Tom'] [20 'w' 'Mary']] 该数据类型为： <class 'numpy.ndarray'>
# Dataframe 创建方法一：由数组/list组成的字典 # 创建方法:pandas.Dataframe() data1 = {'a':[1,2,3], 'b':[3,4,5], 'c':[5,6,7]} data2 = {'one':np.random.rand(3), 'two':np.random.rand(3)} # 这里如果尝试 'two':np.random.rand(4) 会怎么样？ print(data1) print(data2) df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) print(df1) print(df2) # 由数组/list组成的字典 创建Dataframe，columns为字典key，index为默认数字标签 # 字典的值的长度必须保持一致！ df1 = pd.DataFrame(data1, columns = ['b','c','a','d']) print(df1) df1 = pd.DataFrame(data1, columns = ['b','c']) print(df1) # columns参数：可以重新指定列的顺序，格式为list，如果现有数据中没有该列（比如'd'），则产生NaN值 # 如果columns重新指定时候，列的数量可以少于原数据 df2 = pd.DataFrame(data2, index = ['f1','f2','f3']) # 这里如果尝试 index = ['f1','f2','f3','f4'] 会怎么样？ print(df2) # index参数：重新定义index，格式为list，长度必须保持一致
输出：
{'a': [1, 2, 3], 'c': [5, 6, 7], 'b': [3, 4, 5]}
{'one': array([ 0.00101091, 0.08807153, 0.58345056]), 'two': array([ 0.49774634, 0.16782565, 0.76443489])}
a b c
0 1 3 5
1 2 4 6
2 3 5 7
one two
0 0.001011 0.497746
1 0.088072 0.167826
2 0.583451 0.764435
b c a d
0 3 5 1 NaN
1 4 6 2 NaN
2 5 7 3 NaN
b c
0 3 5
1 4 6
2 5 7
one two
f1 0.001011 0.497746
f2 0.088072 0.167826
f3 0.583451 0.764435
# Dataframe 创建方法二：由Series组成的字典 data1 = {'one':pd.Series(np.random.rand(2)), 'two':pd.Series(np.random.rand(3))} # 没有设置index的Series data2 = {'one':pd.Series(np.random.rand(2), index = ['a','b']), 'two':pd.Series(np.random.rand(3),index = ['a','b','c'])} # 设置了index的Series print(data1) print(data2) df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) print(df1) print(df2) # 由Seris组成的字典 创建Dataframe，columns为字典key，index为Series的标签（如果Series没有指定标签，则是默认数字标签） # Series可以长度不一样，生成的Dataframe会出现NaN值
输出：
{'one': 0 0.892580
1 0.834076
dtype: float64, 'two': 0 0.301309
1 0.977709
2 0.489000
dtype: float64}
{'one': a 0.470947
b 0.584577
dtype: float64, 'two': a 0.122659
b 0.136429
c 0.396825
dtype: float64}
one two
0 0.892580 0.301309
1 0.834076 0.977709
2 NaN 0.489000
one two
a 0.470947 0.122659
b 0.584577 0.136429
c NaN 0.396825
# Dataframe 创建方法三：通过二维数组直接创建 ar = np.random.rand(9).reshape(3,3) print(ar) df1 = pd.DataFrame(ar) df2 = pd.DataFrame(ar, index = ['a', 'b', 'c'], columns = ['one','two','three']) # 可以尝试一下index或columns长度不等于已有数组的情况 print(df1) print(df2) # 通过二维数组直接创建Dataframe，得到一样形状的结果数据，如果不指定index和columns，两者均返回默认数字格式 # index和colunms指定长度与原数组保持一致
输出：
[[ 0.54492282 0.28956161 0.46592269] [ 0.30480674 0.12917132 0.38757672] [ 0.2518185 0.13544544 0.13930429]] 0 1 2 0 0.544923 0.289562 0.465923 1 0.304807 0.129171 0.387577 2 0.251819 0.135445 0.139304 one two three a 0.544923 0.289562 0.465923 b 0.304807 0.129171 0.387577 c 0.251819 0.135445 0.139304
# Dataframe 创建方法四：由字典组成的列表 data = [{'one': 1, 'two': 2}, {'one': 5, 'two': 10, 'three': 20}] print(data) df1 = pd.DataFrame(data) df2 = pd.DataFrame(data, index = ['a','b']) df3 = pd.DataFrame(data, columns = ['one','two']) print(df1) print(df2) print(df3) # 由字典组成的列表创建Dataframe，columns为字典的key，index不做指定则为默认数组标签 # colunms和index参数分别重新指定相应列及行标签
输出：
[{'one': 1, 'two': 2}, {'one': 5, 'three': 20, 'two': 10}]
one three two
0 1 NaN 2
1 5 20.0 10
one three two
a 1 NaN 2
b 5 20.0 10
one two
0 1 2
1 5 10
# Dataframe 创建方法五：由字典组成的字典 data = {'Jack':{'math':90,'english':89,'art':78}, 'Marry':{'math':82,'english':95,'art':92}, 'Tom':{'math':78,'english':67}} df1 = pd.DataFrame(data) print(df1) # 由字典组成的字典创建Dataframe，columns为字典的key，index为子字典的key df2 = pd.DataFrame(data, columns = ['Jack','Tom','Bob']) df3 = pd.DataFrame(data, index = ['a','b','c']) print(df2) print(df3) # columns参数可以增加和减少现有列，如出现新的列，值为NaN # index在这里和之前不同，并不能改变原有index，如果指向新的标签，值为NaN （非常重要！）
输出：
Jack Marry Tom art 78 92 NaN english 89 95 67.0 math 90 82 78.0 Jack Tom Bob art 78 NaN NaN english 89 67.0 NaN math 90 78.0 NaN Jack Marry Tom a NaN NaN NaN b NaN NaN NaN c NaN NaN NaN
''' 【课程2.6】 Pandas数据结构Dataframe：索引 Dataframe既有行索引也有列索引，可以被看做由Series组成的字典（共用一个索引） 选择列 / 选择行 / 切片 / 布尔判断 '''
# 选择行与列 df = pd.DataFrame(np.random.rand(12).reshape(3,4)*100, index = ['one','two','three'], columns = ['a','b','c','d']) print(df) data1 = df['a'] data2 = df[['a','c']] print(data1,type(data1)) print(data2,type(data2)) print('-----') # 按照列名选择列，只选择一列输出Series，选择多列输出Dataframe data3 = df.loc['one'] data4 = df.loc[['one','two']] print(data2,type(data3)) print(data3,type(data4)) # 按照index选择行，只选择一行输出Series，选择多行输出Dataframe
输出：
a b c d one 72.615321 49.816987 57.485645 84.226944 two 46.295674 34.480439 92.267989 17.111412 three 14.699591 92.754997 39.683577 93.255880 one 72.615321 two 46.295674 three 14.699591 Name: a, dtype: float64 <class 'pandas.core.series.Series'> a c one 72.615321 57.485645 two 46.295674 92.267989 three 14.699591 39.683577 <class 'pandas.core.frame.DataFrame'> ----- a c one 72.615321 57.485645 two 46.295674 92.267989 three 14.699591 39.683577 <class 'pandas.core.series.Series'> a 72.615321 b 49.816987 c 57.485645 d 84.226944 Name: one, dtype: float64 <class 'pandas.core.frame.DataFrame'>
# df[] - 选择列 # 一般用于选择列，也可以选择行 df = pd.DataFrame(np.random.rand(12).reshape(3,4)*100, index = ['one','two','three'], columns = ['a','b','c','d']) print(df) print('-----') data1 = df['a'] data2 = df[['b','c']] # 尝试输入 data2 = df[['b','c','e']] print(data1) print(data2) # df[]默认选择列，[]中写列名（所以一般数据colunms都会单独制定，不会用默认数字列名，以免和index冲突） # 单选列为Series，print结果为Series格式 # 多选列为Dataframe，print结果为Dataframe格式 data3 = df[:1] #data3 = df[0] #data3 = df['one'] print(data3,type(data3)) # df[]中为数字时，默认选择行，且只能进行切片的选择，不能单独选择（df[0]） # 输出结果为Dataframe，即便只选择一行 # df[]不能通过索引标签名来选择行(df['one']) # 核心笔记：df[col]一般用于选择列，[]中写列名
输出：
a b c d one 88.490183 93.588825 1.605172 74.610087 two 45.905361 49.257001 87.852426 97.490521 three 95.801001 97.991028 74.451954 64.290587 ----- one 88.490183 two 45.905361 three 95.801001 Name: a, dtype: float64 b c one 93.588825 1.605172 two 49.257001 87.852426 three 97.991028 74.451954 a b c d one 88.490183 93.588825 1.605172 74.610087 <class 'pandas.core.frame.DataFrame'>
# df.loc[] - 按index选择行 df1 = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = ['one','two','three','four'], columns = ['a','b','c','d']) df2 = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, columns = ['a','b','c','d']) print(df1) print(df2) print('-----') data1 = df1.loc['one'] data2 = df2.loc[1] print(data1) print(data2) print('单标签索引\n-----') # 单个标签索引，返回Series data3 = df1.loc[['two','three','five']] data4 = df2.loc[[3,2,1]] print(data3) print(data4) print('多标签索引\n-----') # 多个标签索引，如果标签不存在，则返回NaN # 顺序可变 data5 = df1.loc['one':'three'] data6 = df2.loc[1:3] print(data5) print(data6) print('切片索引') # 可以做切片对象 # 末端包含 # 核心笔记：df.loc[label]主要针对index选择行，同时支持指定index，及默认数字index
输出：
a b c d one 73.070679 7.169884 80.820532 62.299367 two 34.025462 77.849955 96.160170 55.159017 three 27.897582 39.595687 69.280955 49.477429 four 76.723039 44.995970 22.408450 23.273089 a b c d 0 93.871055 28.031989 57.093181 34.695293 1 22.882809 47.499852 86.466393 86.140909 2 80.840336 98.120735 84.495414 8.413039 3 59.695834 1.478707 15.069485 48.775008 ----- a 73.070679 b 7.169884 c 80.820532 d 62.299367 Name: one, dtype: float64 a 22.882809 b 47.499852 c 86.466393 d 86.140909 Name: 1, dtype: float64 单标签索引 ----- a b c d two 34.025462 77.849955 96.160170 55.159017 three 27.897582 39.595687 69.280955 49.477429 five NaN NaN NaN NaN a b c d 3 59.695834 1.478707 15.069485 48.775008 2 80.840336 98.120735 84.495414 8.413039 1 22.882809 47.499852 86.466393 86.140909 多标签索引 ----- a b c d one 73.070679 7.169884 80.820532 62.299367 two 34.025462 77.849955 96.160170 55.159017 three 27.897582 39.595687 69.280955 49.477429 a b c d 1 22.882809 47.499852 86.466393 86.140909 2 80.840336 98.120735 84.495414 8.413039 3 59.695834 1.478707 15.069485 48.775008 切片索引
# df.iloc[] - 按照整数位置（从轴的0到length-1）选择行 # 类似list的索引，其顺序就是dataframe的整数位置，从0开始计 df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = ['one','two','three','four'], columns = ['a','b','c','d']) print(df) print('------') print(df.iloc[0]) print(df.iloc[-1]) #print(df.iloc[4]) print('单位置索引\n-----') # 单位置索引 # 和loc索引不同，不能索引超出数据行数的整数位置 print(df.iloc[[0,2]]) print(df.iloc[[3,2,1]]) print('多位置索引\n-----') # 多位置索引 # 顺序可变 print(df.iloc[1:3]) print(df.iloc[::2]) print('切片索引') # 切片索引 # 末端不包含
输出：
a b c d one 21.848926 2.482328 17.338355 73.014166 two 99.092794 0.601173 18.598736 61.166478 three 87.183015 85.973426 48.839267 99.930097 four 75.007726 84.208576 69.445779 75.546038 ------ a 21.848926 b 2.482328 c 17.338355 d 73.014166 Name: one, dtype: float64 a 75.007726 b 84.208576 c 69.445779 d 75.546038 Name: four, dtype: float64 单位置索引 ----- a b c d one 21.848926 2.482328 17.338355 73.014166 three 87.183015 85.973426 48.839267 99.930097 a b c d four 75.007726 84.208576 69.445779 75.546038 three 87.183015 85.973426 48.839267 99.930097 two 99.092794 0.601173 18.598736 61.166478 多位置索引 ----- a b c d two 99.092794 0.601173 18.598736 61.166478 three 87.183015 85.973426 48.839267 99.930097 a b c d one 21.848926 2.482328 17.338355 73.014166 three 87.183015 85.973426 48.839267 99.930097 切片索引
# 布尔型索引 # 和Series原理相同 df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = ['one','two','three','four'], columns = ['a','b','c','d']) print(df) print('------') b1 = df < 20 print(b1,type(b1)) print(df[b1]) # 也可以书写为 df[df < 20] print('------') # 不做索引则会对数据每个值进行判断 # 索引结果保留 所有数据：True返回原数据，False返回值为NaN b2 = df['a'] > 50 print(b2,type(b2)) print(df[b2]) # 也可以书写为 df[df['a'] > 50] print('------') # 单列做判断 # 索引结果保留 单列判断为True的行数据，包括其他列 b3 = df[['a','b']] > 50 print(b3,type(b3)) print(df[b3]) # 也可以书写为 df[df[['a','b']] > 50] print('------') # 多列做判断 # 索引结果保留 所有数据：True返回原数据，False返回值为NaN b4 = df.loc[['one','three']] < 50 print(b4,type(b4)) print(df[b4]) # 也可以书写为 df[df.loc[['one','three']] < 50] print('------') # 多行做判断 # 索引结果保留 所有数据：True返回原数据，False返回值为NaN
输出：
a b c d one 19.185849 20.303217 21.800384 45.189534 two 50.105112 28.478878 93.669529 90.029489 three 35.496053 19.248457 74.811841 20.711431 four 24.604478 57.731456 49.682717 82.132866 ------ a b c d one True False False False two False False False False three False True False False four False False False False <class 'pandas.core.frame.DataFrame'> a b c d one 19.185849 NaN NaN NaN two NaN NaN NaN NaN three NaN 19.248457 NaN NaN four NaN NaN NaN NaN ------ one False two True three False four False Name: a, dtype: bool <class 'pandas.core.series.Series'> a b c d two 50.105112 28.478878 93.669529 90.029489 ------ a b one False False two True False three False False four False True <class 'pandas.core.frame.DataFrame'> a b c d one NaN NaN NaN NaN two 50.105112 NaN NaN NaN three NaN NaN NaN NaN four NaN 57.731456 NaN NaN ------ a b c d one True True True True three True True False True <class 'pandas.core.frame.DataFrame'> a b c d one 19.185849 20.303217 21.800384 45.189534 two NaN NaN NaN NaN three 35.496053 19.248457 NaN 20.711431 four NaN NaN NaN NaN
# 多重索引：比如同时索引行和列 # 先选择列再选择行 —— 相当于对于一个数据，先筛选字段，再选择数据量 df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = ['one','two','three','four'], columns = ['a','b','c','d']) print(df) print('------') print(df['a'].loc[['one','three']]) # 选择a列的one，three行 print(df[['b','c','d']].iloc[::2]) # 选择b，c，d列的one，three行 print(df[df['a'] < 50].iloc[:2]) # 选择满足判断索引的前两行数据
输出：
a b c d one 50.660904 89.827374 51.096827 3.844736 two 70.699721 78.750014 52.988276 48.833037 three 33.653032 27.225202 24.864712 29.662736 four 21.792339 26.450939 6.122134 52.323963 ------ one 50.660904 three 33.653032 Name: a, dtype: float64 b c d one 89.827374 51.096827 3.844736 three 27.225202 24.864712 29.662736 a b c d three 33.653032 27.225202 24.864712 29.662736 four 21.792339 26.450939 6.122134 52.323963
''' 【课程2.7】 Pandas数据结构Dataframe：基本技巧 数据查看、转置 / 添加、修改、删除值 / 对齐 / 排序 '''
# 数据查看、转置 df = pd.DataFrame(np.random.rand(16).reshape(8,2)*100, columns = ['a','b']) print(df.head(2)) print(df.tail()) # .head()查看头部数据 # .tail()查看尾部数据 # 默认查看5条 print(df.T) # .T 转置
输出：
a b 0 5.777208 18.374283 1 85.961515 55.120036 a b 3 21.236577 15.902872 4 46.137564 29.350647 5 70.157709 58.972728 6 8.368292 42.011356 7 29.824574 87.062295 0 1 2 3 4 5 \ a 5.777208 85.961515 11.005284 21.236577 46.137564 70.157709 b 18.374283 55.120036 35.595598 15.902872 29.350647 58.972728 6 7 a 8.368292 29.824574 b 42.011356 87.062295
# 添加与修改 df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, columns = ['a','b','c','d']) print(df) df['e'] = 10 df.loc[4] = 20 print(df) # 新增列/行并赋值 df['e'] = 20 df[['a','c']] = 100 print(df) # 索引后直接修改值
输出：
a b c d 0 17.148791 73.833921 39.069417 5.675815 1 91.572695 66.851601 60.320698 92.071097 2 79.377105 24.314520 44.406357 57.313429 3 84.599206 61.310945 3.916679 30.076458 a b c d e 0 17.148791 73.833921 39.069417 5.675815 10 1 91.572695 66.851601 60.320698 92.071097 10 2 79.377105 24.314520 44.406357 57.313429 10 3 84.599206 61.310945 3.916679 30.076458 10 4 20.000000 20.000000 20.000000 20.000000 20 a b c d e 0 100 73.833921 100 5.675815 20 1 100 66.851601 100 92.071097 20 2 100 24.314520 100 57.313429 20 3 100 61.310945 100 30.076458 20 4 100 20.000000 100 20.000000 20
# 删除 del / drop() df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, columns = ['a','b','c','d']) print(df) del df['a'] print(df) print('-----') # del语句 - 删除列 print(df.drop(0)) print(df.drop([1,2])) print(df) print('-----') # drop()删除行，inplace=False → 删除后生成新的数据，不改变原数据 print(df.drop(['d'], axis = 1)) print(df) # drop()删除列，需要加上axis = 1，inplace=False → 删除后生成新的数据，不改变原数据
输出：
a b c d 0 91.866806 88.753655 18.469852 71.651277 1 64.835568 33.844967 6.391246 54.916094 2 75.930985 19.169862 91.042457 43.648258 3 15.863853 24.788866 10.625684 82.135316 b c d 0 88.753655 18.469852 71.651277 1 33.844967 6.391246 54.916094 2 19.169862 91.042457 43.648258 3 24.788866 10.625684 82.135316 ----- b c d 1 33.844967 6.391246 54.916094 2 19.169862 91.042457 43.648258 3 24.788866 10.625684 82.135316 b c d 0 88.753655 18.469852 71.651277 3 24.788866 10.625684 82.135316 b c d 0 88.753655 18.469852 71.651277 1 33.844967 6.391246 54.916094 2 19.169862 91.042457 43.648258 3 24.788866 10.625684 82.135316 ----- b c 0 88.753655 18.469852 1 33.844967 6.391246 2 19.169862 91.042457 3 24.788866 10.625684 b c d 0 88.753655 18.469852 71.651277 1 33.844967 6.391246 54.916094 2 19.169862 91.042457 43.648258 3 24.788866 10.625684 82.135316
# 对齐 df1 = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C']) print(df1 + df2) # DataFrame对象之间的数据自动按照列和索引（行标签）对齐
输出：
A B C D 0 -0.281123 -2.529461 1.325663 NaN 1 -0.310514 -0.408225 -0.760986 NaN 2 -0.172169 -2.355042 1.521342 NaN 3 1.113505 0.325933 3.689586 NaN 4 0.107513 -0.503907 -1.010349 NaN 5 -0.845676 -2.410537 -1.406071 NaN 6 1.682854 -0.576620 -0.981622 NaN 7 NaN NaN NaN NaN 8 NaN NaN NaN NaN 9 NaN NaN NaN NaN
# 排序1 - 按值排序 .sort_values # 同样适用于Series df1 = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, columns = ['a','b','c','d']) print(df1) print(df1.sort_values(['a'], ascending = True)) # 升序 print(df1.sort_values(['a'], ascending = False)) # 降序 print('------') # ascending参数：设置升序降序，默认升序 # 单列排序 df2 = pd.DataFrame({'a':[1,1,1,1,2,2,2,2], 'b':list(range(8)), 'c':list(range(8,0,-1))}) print(df2) print(df2.sort_values(['a','c'])) # 多列排序，按列顺序排序
输出：
a b c d 0 16.519099 19.601879 35.464189 58.866972 1 34.506472 97.106578 96.308244 54.049359 2 87.177828 47.253416 92.098847 19.672678 3 66.673226 51.969534 71.789055 14.504191 a b c d 0 16.519099 19.601879 35.464189 58.866972 1 34.506472 97.106578 96.308244 54.049359 3 66.673226 51.969534 71.789055 14.504191 2 87.177828 47.253416 92.098847 19.672678 a b c d 2 87.177828 47.253416 92.098847 19.672678 3 66.673226 51.969534 71.789055 14.504191 1 34.506472 97.106578 96.308244 54.049359 0 16.519099 19.601879 35.464189 58.866972 ------ a b c 0 1 0 8 1 1 1 7 2 1 2 6 3 1 3 5 4 2 4 4 5 2 5 3 6 2 6 2 7 2 7 1 a b c 3 1 3 5 2 1 2 6 1 1 1 7 0 1 0 8 7 2 7 1 6 2 6 2 5 2 5 3 4 2 4 4
# 排序2 - 索引排序 .sort_index df1 = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = [5,4,3,2], columns = ['a','b','c','d']) df2 = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index = ['h','s','x','g'], columns = ['a','b','c','d']) print(df1) print(df1.sort_index()) print(df2) print(df2.sort_index()) # 按照index排序 # 默认 ascending=True, inplace=False
输出：
a b c d 5 57.327269 87.623119 93.655538 5.859571 4 69.739134 80.084366 89.005538 56.825475 3 88.148296 6.211556 68.938504 41.542563 2 29.248036 72.005306 57.855365 45.931715 a b c d 2 29.248036 72.005306 57.855365 45.931715 3 88.148296 6.211556 68.938504 41.542563 4 69.739134 80.084366 89.005538 56.825475 5 57.327269 87.623119 93.655538 5.859571 a b c d h 50.579469 80.239138 24.085110 39.443600 s 30.906725 39.175302 11.161542 81.010205 x 19.900056 18.421110 4.995141 12.605395 g 67.760755 72.573568 33.507090 69.854906 a b c d g 67.760755 72.573568 33.507090 69.854906 h 50.579469 80.239138 24.085110 39.443600 s 30.906725 39.175302 11.161542 81.010205 x 19.900056 18.421110 4.995141 12.605395
