import pandas as pd
import numpy as np

pd.__version__

'1.0.3'

文件的读写#

csv格式

data=pd.read_csv('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\table.csv')
data.head(20)
School Class ID Gender Address Height Weight Math Physics
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
2 S_1 C_1 1103 M street_2 186 82 87.2 B+
3 S_1 C_1 1104 F street_2 167 81 80.4 B-
4 S_1 C_1 1105 F street_4 159 64 84.8 B+
5 S_1 C_2 1201 M street_5 188 68 97.0 A-
6 S_1 C_2 1202 F street_4 176 94 63.5 B-
7 S_1 C_2 1203 M street_6 160 53 58.8 A+
8 S_1 C_2 1204 F street_5 162 63 33.8 B
9 S_1 C_2 1205 F street_6 167 63 68.4 B-
10 S_1 C_3 1301 M street_4 161 68 31.5 B+
11 S_1 C_3 1302 F street_1 175 57 87.7 A-
12 S_1 C_3 1303 M street_7 188 82 49.7 B
13 S_1 C_3 1304 M street_2 195 70 85.2 A
14 S_1 C_3 1305 F street_5 187 69 61.7 B-
15 S_2 C_1 2101 M street_7 174 84 83.3 C
16 S_2 C_1 2102 F street_6 161 61 50.6 B+
17 S_2 C_1 2103 M street_4 157 61 52.5 B-
18 S_2 C_1 2104 F street_5 159 97 72.2 B+
19 S_2 C_1 2105 M street_4 170 81 34.2 A

txt格式

data_txt=pd.read_table('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\table.txt')
#读取文件的时候，默认是以，作为分隔符，可以指定其他分隔符
data_txt.head(20)
col1 col2 col3 col4
0 2 a 1.4 apple
1 3 b 3.4 banana
2 6 c 2.5 orange
3 5 d 3.2 lemon

xls或者xlsx格式

data_excel=pd.read_excel('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\table.xlsx')
data_excel.head(20)
School Class ID Gender Address Height Weight Math Physics
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
2 S_1 C_1 1103 M street_2 186 82 87.2 B+
3 S_1 C_1 1104 F street_2 167 81 80.4 B-
4 S_1 C_1 1105 F street_4 159 64 84.8 B+
5 S_1 C_2 1201 M street_5 188 68 97.0 A-
6 S_1 C_2 1202 F street_4 176 94 63.5 B-
7 S_1 C_2 1203 M street_6 160 53 58.8 A+
8 S_1 C_2 1204 F street_5 162 63 33.8 B
9 S_1 C_2 1205 F street_6 167 63 68.4 B-
10 S_1 C_3 1301 M street_4 161 68 31.5 B+
11 S_1 C_3 1302 F street_1 175 57 87.7 A-
12 S_1 C_3 1303 M street_7 188 82 49.7 B
13 S_1 C_3 1304 M street_2 195 70 85.2 A
14 S_1 C_3 1305 F street_5 187 69 61.7 B-
15 S_2 C_1 2101 M street_7 174 84 83.3 C
16 S_2 C_1 2102 F street_6 161 61 50.6 B+
17 S_2 C_1 2103 M street_4 157 61 52.5 B-
18 S_2 C_1 2104 F street_5 159 97 72.2 B+
19 S_2 C_1 2105 M street_4 170 81 34.2 A

写入

csv格式

data.to_csv('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\table.csv')

其他的不过多展示了

数据的基本结构

Series

创建一个Series

  • 对于一个Series，其中最常用的属性为值（values）,索引（index），名字（name）,类型（type）
s=pd.Series(np.random.randn(5),index=['a','b','c','d','e'],name='this is Series',dtype='float64')
s

a   -1.576202
b    1.070319
c    0.668490
d    1.760701
e    0.942655
Name: this is Series, dtype: float64

访问Series属性

s.values

array([-1.57620183,  1.07031892,  0.66849049,  1.76070095,  0.94265474])

都会就不过多敲代码了

取出元素

#根据索引来取出
s['a']

-1.5762018254215895

调用方法

print([attr for attr in dir(s) if not attr.startswith('_')])
#字段名称打印？

['T', 'a', 'abs', 'add', 'add_prefix', 'add_suffix', 'agg', 'aggregate', 'align', 'all', 'any', 'append', 'apply', 'argmax', 'argmin', 'argsort', 'array', 'asfreq', 'asof', 'astype', 'at', 'at_time', 'attrs', 'autocorr', 'axes', 'b', 'between', 'between_time', 'bfill', 'bool', 'c', 'clip', 'combine', 'combine_first', 'convert_dtypes', 'copy', 'corr', 'count', 'cov', 'cummax', 'cummin', 'cumprod', 'cumsum', 'd', 'describe', 'diff', 'div', 'divide', 'divmod', 'dot', 'drop', 'drop_duplicates', 'droplevel', 'dropna', 'dtype', 'dtypes', 'duplicated', 'e', 'empty', 'eq', 'equals', 'ewm', 'expanding', 'explode', 'factorize', 'ffill', 'fillna', 'filter', 'first', 'first_valid_index', 'floordiv', 'ge', 'get', 'groupby', 'gt', 'hasnans', 'head', 'hist', 'iat', 'idxmax', 'idxmin', 'iloc', 'index', 'infer_objects', 'interpolate', 'is_monotonic', 'is_monotonic_decreasing', 'is_monotonic_increasing', 'is_unique', 'isin', 'isna', 'isnull', 'item', 'items', 'iteritems', 'keys', 'kurt', 'kurtosis', 'last', 'last_valid_index', 'le', 'loc', 'lt', 'mad', 'map', 'mask', 'max', 'mean', 'median', 'memory_usage', 'min', 'mod', 'mode', 'mul', 'multiply', 'name', 'nbytes', 'ndim', 'ne', 'nlargest', 'notna', 'notnull', 'nsmallest', 'nunique', 'pct_change', 'pipe', 'plot', 'pop', 'pow', 'prod', 'product', 'quantile', 'radd', 'rank', 'ravel', 'rdiv', 'rdivmod', 'reindex', 'reindex_like', 'rename', 'rename_axis', 'reorder_levels', 'repeat', 'replace', 'resample', 'reset_index', 'rfloordiv', 'rmod', 'rmul', 'rolling', 'round', 'rpow', 'rsub', 'rtruediv', 'sample', 'searchsorted', 'sem', 'set_axis', 'shape', 'shift', 'size', 'skew', 'slice_shift', 'sort_index', 'sort_values', 'squeeze', 'std', 'sub', 'subtract', 'sum', 'swapaxes', 'swaplevel', 'tail', 'take', 'to_clipboard', 'to_csv', 'to_dict', 'to_excel', 'to_frame', 'to_hdf', 'to_json', 'to_latex', 'to_list', 'to_markdown', 'to_numpy', 'to_period', 'to_pickle', 'to_sql', 'to_string', 'to_timestamp', 'to_xarray', 'transform', 'transpose', 'truediv', 'truncate', 'tshift', 'tz_convert', 'tz_localize', 'unique', 'unstack', 'update', 'value_counts', 'values', 'var', 'view', 'where', 'xs']

DataFrame

创建一个DF

df=pd.DataFrame({'col1':list('abcde'),'col2':range(5,10),'col3':[1.3,2.5,3.6,4.6,5.8]},index=list('12345'))
df
col1 col2 col3
1 a 5 1.3
2 b 6 2.5
3 c 7 3.6
4 d 8 4.6
5 e 9 5.8

从DF中取出一列为Series

df['col1']

1    a
2    b
3    c
4    d
5    e
Name: col1, dtype: object

type(df)

pandas.core.frame.DataFrame

type(df['col1'])

pandas.core.series.Series

索引对齐

df1=pd.DataFrame({"A":[1,2,3]},index=[1,2,3])
df2=pd.DataFrame({'A':[1,2,3]},index=[3,1,2])
df1-df2
#因为存在索引对齐，所以结果不是0
A
1 -1
2 -1
3 2

列的删除与添加

  • 可以使用drop函数，del函数与pop函数
df.drop(index='5',columns='col1')
col2 col3
1 5 1.3
2 6 2.5
3 7 3.6
4 8 4.6 
#pop方法直接在DF上操作，且返回被删除的列，与Python中的pop函数类似
df['col1']=[1,2,3,4,5]
df.pop('col1')

1    1
2    2
3    3
4    4
5    5
Name: col1, dtype: int64

df
col2 col3
1 5 1.3
2 6 2.5
3 7 3.6
4 8 4.6
5 9 5.8 
#可以直接增加，也可通过assign可以增加新列
df1['B']=list('abc')
df1
A B
1 1 a
2 2 b
3 3 c 
df1.assign(C=pd.Series(list('def')))
#assign方法不会对DF进行修改，所以要重新给予新的变量
A B C
1 1 a e
2 2 b f
3 3 c NaN

根据不同类型选择列

df.select_dtypes(include=['number']).head()
col2 col3
1 5 1.3
2 6 2.5
3 7 3.6
4 8 4.6
5 9 5.8

将series转换成DF

s=df.mean()
s.name='to_DF'
s

col2    7.00
col3    3.56
Name: to_DF, dtype: float64

s.to_frame()
to_DF
col2 7.00
col3 3.56 
#使用T进行转置
s.to_frame().T
col2 col3
to_DF 7.0 3.56

常用的基本函数

df=pd.read_csv('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\table.csv')
df=df.drop(columns='Unnamed: 0')
df
School Class ID Gender Address Height Weight Math Physics
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
2 S_1 C_1 1103 M street_2 186 82 87.2 B+
3 S_1 C_1 1104 F street_2 167 81 80.4 B-
4 S_1 C_1 1105 F street_4 159 64 84.8 B+
5 S_1 C_2 1201 M street_5 188 68 97.0 A-
6 S_1 C_2 1202 F street_4 176 94 63.5 B-
7 S_1 C_2 1203 M street_6 160 53 58.8 A+
8 S_1 C_2 1204 F street_5 162 63 33.8 B
9 S_1 C_2 1205 F street_6 167 63 68.4 B-
10 S_1 C_3 1301 M street_4 161 68 31.5 B+
11 S_1 C_3 1302 F street_1 175 57 87.7 A-
12 S_1 C_3 1303 M street_7 188 82 49.7 B
13 S_1 C_3 1304 M street_2 195 70 85.2 A
14 S_1 C_3 1305 F street_5 187 69 61.7 B-
15 S_2 C_1 2101 M street_7 174 84 83.3 C
16 S_2 C_1 2102 F street_6 161 61 50.6 B+
17 S_2 C_1 2103 M street_4 157 61 52.5 B-
18 S_2 C_1 2104 F street_5 159 97 72.2 B+
19 S_2 C_1 2105 M street_4 170 81 34.2 A
20 S_2 C_2 2201 M street_5 193 100 39.1 B
21 S_2 C_2 2202 F street_7 194 77 68.5 B+
22 S_2 C_2 2203 M street_4 155 91 73.8 A+
23 S_2 C_2 2204 M street_1 175 74 47.2 B-
24 S_2 C_2 2205 F street_7 183 76 85.4 B
25 S_2 C_3 2301 F street_4 157 78 72.3 B+
26 S_2 C_3 2302 M street_5 171 88 32.7 A
27 S_2 C_3 2303 F street_7 190 99 65.9 C
28 S_2 C_3 2304 F street_6 164 81 95.5 A-
29 S_2 C_3 2305 M street_4 187 73 48.9 B
30 S_2 C_4 2401 F street_2 192 62 45.3 A
31 S_2 C_4 2402 M street_7 166 82 48.7 B
32 S_2 C_4 2403 F street_6 158 60 59.7 B+
33 S_2 C_4 2404 F street_2 160 84 67.7 B
34 S_2 C_4 2405 F street_6 193 54 47.6 B

unique和nunique

nunique显示有多少个唯一值 —数量

df['Physics'].nunique()

7

unique显示所有的唯一值 —具体值

df['Physics'].unique()

array(['A+', 'B+', 'B-', 'A-', 'B', 'A', 'C'], dtype=object)

count and value_counts

df['Physics'].count()
#注意，返回的是非缺失值的元素个数！

35

#value_counts返回每个元素有多少个
df['Physics'].value_counts()

B+    9
B     8
B-    6
A     4
A+    3
A-    3
C     2
Name: Physics, dtype: int64

describe and info

#info函数范围有哪些列，有多少非缺失值，每列的类型
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 35 entries, 0 to 34
Data columns (total 9 columns):#   Column   Non-Null Count  Dtype
---  ------   --------------  -----  0   School   35 non-null     object 1   Class    35 non-null     object 2   ID       35 non-null     int64  3   Gender   35 non-null     object 4   Address  35 non-null     object 5   Height   35 non-null     int64  6   Weight   35 non-null     int64  7   Math     35 non-null     float648   Physics  35 non-null     object
dtypes: float64(1), int64(3), object(5)
memory usage: 2.6+ KB

#describe默认统计数值型数据的各个统计量
df.describe()
ID Height Weight Math
count 35.00000 35.000000 35.000000 35.000000
mean 1803.00000 174.142857 74.657143 61.351429
std 536.87741 13.541098 12.895377 19.915164
min 1101.00000 155.000000 53.000000 31.500000
25% 1204.50000 161.000000 63.000000 47.400000
50% 2103.00000 173.000000 74.000000 61.700000
75% 2301.50000 187.500000 82.000000 77.100000
max 2405.00000 195.000000 100.000000 97.000000 
#describe函数可以调整不同的分位数
df.describe(percentiles=[.05,.25,.75,.95])#注意是[]
ID Height Weight Math
count 35.00000 35.000000 35.000000 35.000000
mean 1803.00000 174.142857 74.657143 61.351429
std 536.87741 13.541098 12.895377 19.915164
min 1101.00000 155.000000 53.000000 31.500000
5% 1102.70000 157.000000 56.100000 32.640000
25% 1204.50000 161.000000 63.000000 47.400000
50% 2103.00000 173.000000 74.000000 61.700000
75% 2301.50000 187.500000 82.000000 77.100000
95% 2403.30000 193.300000 97.600000 90.040000
max 2405.00000 195.000000 100.000000 97.000000 
#对于非数值型也可以使用describe函数
df['Physics'].describe()

count     35
unique     7
top       B+
freq       9
Name: Physics, dtype: object

idxmax and nlargest

# idxmax函数返回最大值，在某些情况下使用，同理类比idxmin,
df['Math'].idxmax()
df['Math'].idxmin()

10

#nlargest函数范围前几个大的元素值，nsmallest功能类似
df['Math'].nlargest()#（）可以填写数量

5     97.0
28    95.5
11    87.7
2     87.2
24    85.4
Name: Math, dtype: float64

clip and repalce

#clip是对超过或低于某些数进行截断
df['Math'].clip(33,80).head(30)

0     34.0
1     33.0
2     80.0
3     80.0
4     80.0
5     80.0
6     63.5
7     58.8
8     33.8
9     68.4
10    33.0
11    80.0
12    49.7
13    80.0
14    61.7
15    80.0
16    50.6
17    52.5
18    72.2
19    34.2
20    39.1
21    68.5
22    73.8
23    47.2
24    80.0
25    72.3
26    33.0
27    65.9
28    80.0
29    48.9
Name: Math, dtype: float64

#repalce是对某些值进行替换
df['Address'].head()

0    street_1
1    street_2
2    street_2
3    street_2
4    street_4
Name: Address, dtype: object

df['Address'].repalce(['street_1','street_2'],['one','two']).head()

---------------------------------------------------------------------------AttributeError                            Traceback (most recent call last)<ipython-input-97-4b99be6262e7> in <module>
----> 1 df['Address'].repalce(['street_1','street_2'],['one','two']).head()C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)5272             if self._info_axis._can_hold_identifiers_and_holds_name(name):5273                 return self[name]
-> 5274             return object.__getattribute__(self, name)5275 5276     def __setattr__(self, name: str, value) -> None:AttributeError: 'Series' object has no attribute 'repalce'

apply函数

#对于series,它可以迭代每一列值操作
df['Math'].apply(lambda x:str(x)+'!').head()

0    34.0!
1    32.5!
2    87.2!
3    80.4!
4    84.8!
Name: Math, dtype: object

#对于DF，迭代每一列操作
df.apply(lambda x:x.apply(lambda x:str(x)+'!')).head()
School Class ID Gender Address Height Weight Math Physics
0 S_1! C_1! 1101! M! street_1! 173! 63! 34.0! A+!
1 S_1! C_1! 1102! F! street_2! 192! 73! 32.5! B+!
2 S_1! C_1! 1103! M! street_2! 186! 82! 87.2! B+!
3 S_1! C_1! 1104! F! street_2! 167! 81! 80.4! B-!
4 S_1! C_1! 1105! F! street_4! 159! 64! 84.8! B+! 
##对于apply的扩展-可以选择某些列单独加
df['Math'].apply(lambda x:str(x)+"!")

0     34.0!
1     32.5!
2     87.2!
3     80.4!
4     84.8!
5     97.0!
6     63.5!
7     58.8!
8     33.8!
9     68.4!
10    31.5!
11    87.7!
12    49.7!
13    85.2!
14    61.7!
15    83.3!
16    50.6!
17    52.5!
18    72.2!
19    34.2!
20    39.1!
21    68.5!
22    73.8!
23    47.2!
24    85.4!
25    72.3!
26    32.7!
27    65.9!
28    95.5!
29    48.9!
30    45.3!
31    48.7!
32    59.7!
33    67.7!
34    47.6!
Name: Math, dtype: object

排序

索引排序

df.set_index('Math').head(20)
#将Math设为索引
School Class ID Gender Address Height Weight Physics
Math
34.0 S_1 C_1 1101 M street_1 173 63 A+
32.5 S_1 C_1 1102 F street_2 192 73 B+
87.2 S_1 C_1 1103 M street_2 186 82 B+
80.4 S_1 C_1 1104 F street_2 167 81 B-
84.8 S_1 C_1 1105 F street_4 159 64 B+
97.0 S_1 C_2 1201 M street_5 188 68 A-
63.5 S_1 C_2 1202 F street_4 176 94 B-
58.8 S_1 C_2 1203 M street_6 160 53 A+
33.8 S_1 C_2 1204 F street_5 162 63 B
68.4 S_1 C_2 1205 F street_6 167 63 B-
31.5 S_1 C_3 1301 M street_4 161 68 B+
87.7 S_1 C_3 1302 F street_1 175 57 A-
49.7 S_1 C_3 1303 M street_7 188 82 B
85.2 S_1 C_3 1304 M street_2 195 70 A
61.7 S_1 C_3 1305 F street_5 187 69 B-
83.3 S_2 C_1 2101 M street_7 174 84 C
50.6 S_2 C_1 2102 F street_6 161 61 B+
52.5 S_2 C_1 2103 M street_4 157 61 B-
72.2 S_2 C_1 2104 F street_5 159 97 B+
34.2 S_2 C_1 2105 M street_4 170 81 A 
df.set_index('Math').sort_index().head()
School Class ID Gender Address Height Weight Physics
Math
31.5 S_1 C_3 1301 M street_4 161 68 B+
32.5 S_1 C_1 1102 F street_2 192 73 B+
32.7 S_2 C_3 2302 M street_5 171 88 A
33.8 S_1 C_2 1204 F street_5 162 63 B
34.0 S_1 C_1 1101 M street_1 173 63 A+

值排序

df.sort_values(by='Class').head(20)
School Class ID Gender Address Height Weight Math Physics
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
19 S_2 C_1 2105 M street_4 170 81 34.2 A
18 S_2 C_1 2104 F street_5 159 97 72.2 B+
16 S_2 C_1 2102 F street_6 161 61 50.6 B+
15 S_2 C_1 2101 M street_7 174 84 83.3 C
17 S_2 C_1 2103 M street_4 157 61 52.5 B-
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
2 S_1 C_1 1103 M street_2 186 82 87.2 B+
3 S_1 C_1 1104 F street_2 167 81 80.4 B-
4 S_1 C_1 1105 F street_4 159 64 84.8 B+
6 S_1 C_2 1202 F street_4 176 94 63.5 B-
24 S_2 C_2 2205 F street_7 183 76 85.4 B
23 S_2 C_2 2204 M street_1 175 74 47.2 B-
22 S_2 C_2 2203 M street_4 155 91 73.8 A+
21 S_2 C_2 2202 F street_7 194 77 68.5 B+
5 S_1 C_2 1201 M street_5 188 68 97.0 A-
20 S_2 C_2 2201 M street_5 193 100 39.1 B
9 S_1 C_2 1205 F street_6 167 63 68.4 B-
8 S_1 C_2 1204 F street_5 162 63 33.8 B
7 S_1 C_2 1203 M street_6 160 53 58.8 A+

多个值排序，即先对第一层排，在第一层相同的情况下，对第二层排序

df.sort_values(by=['Math','Physics']).head(20)
School Class ID Gender Address Height Weight Math Physics
10 S_1 C_3 1301 M street_4 161 68 31.5 B+
1 S_1 C_1 1102 F street_2 192 73 32.5 B+
26 S_2 C_3 2302 M street_5 171 88 32.7 A
8 S_1 C_2 1204 F street_5 162 63 33.8 B
0 S_1 C_1 1101 M street_1 173 63 34.0 A+
19 S_2 C_1 2105 M street_4 170 81 34.2 A
20 S_2 C_2 2201 M street_5 193 100 39.1 B
30 S_2 C_4 2401 F street_2 192 62 45.3 A
23 S_2 C_2 2204 M street_1 175 74 47.2 B-
34 S_2 C_4 2405 F street_6 193 54 47.6 B
31 S_2 C_4 2402 M street_7 166 82 48.7 B
29 S_2 C_3 2305 M street_4 187 73 48.9 B
12 S_1 C_3 1303 M street_7 188 82 49.7 B
16 S_2 C_1 2102 F street_6 161 61 50.6 B+
17 S_2 C_1 2103 M street_4 157 61 52.5 B-
7 S_1 C_2 1203 M street_6 160 53 58.8 A+
32 S_2 C_4 2403 F street_6 158 60 59.7 B+
14 S_1 C_3 1305 F street_5 187 69 61.7 B-
6 S_1 C_2 1202 F street_4 176 94 63.5 B-
27 S_2 C_3 2303 F street_7 190 99 65.9 C

问题与练习

问题

Series和DataFrame有哪些常见属性和方法？

  • for Series have values,name,type and index
  • for DF index and values

value_counts会统计缺失值吗？

  • NO

与idxmax和nlargest功能相反的是哪两组函数？

  • idxmin and nsmallest

df.mean(axis=1)是什么意思？它与df.mean()的结果一样吗？第一问提到的函数也有axis参数吗？怎么使用？

  • 针对DF进行行均值计算 不一样，前者是行，后者是列

练习

现有一份关于美剧《权力的游戏》剧本的数据集，请解决以下问题：

  • 在所有的数据中，一共出现了多少人物？
  • 以单元格计数（即简单把一个单元格视作一句），谁说了最多的话？
  • 以单词计数，谁说了最多的单词？
data1=pd.read_csv('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\Game_of_Thrones_Script.csv')
data1.head(20)
Release Date Season Episode Episode Title Name Sentence
0 2011/4/17 Season 1 Episode 1 Winter is Coming waymar royce What do you expect? They're savages. One lot s...
1 2011/4/17 Season 1 Episode 1 Winter is Coming will I've never seen wildlings do a thing like this...
2 2011/4/17 Season 1 Episode 1 Winter is Coming waymar royce How close did you get?
3 2011/4/17 Season 1 Episode 1 Winter is Coming will Close as any man would.
4 2011/4/17 Season 1 Episode 1 Winter is Coming gared We should head back to the wall.
5 2011/4/17 Season 1 Episode 1 Winter is Coming royce Do the dead frighten you?
6 2011/4/17 Season 1 Episode 1 Winter is Coming gared Our orders were to track the wildlings. We tra...
7 2011/4/17 Season 1 Episode 1 Winter is Coming royce You don't think he'll ask us how they died? Ge...
8 2011/4/17 Season 1 Episode 1 Winter is Coming will Whatever did it to them could do it to us. The...
9 2011/4/17 Season 1 Episode 1 Winter is Coming royce It's a good thing we're not children. You want...
10 2011/4/17 Season 1 Episode 1 Winter is Coming royce Your dead men seem to have moved camp.
11 2011/4/17 Season 1 Episode 1 Winter is Coming will They were here.
12 2011/4/17 Season 1 Episode 1 Winter is Coming gared See where they went.
13 2011/4/17 Season 1 Episode 1 Winter is Coming royce What is it?
14 2011/4/17 Season 1 Episode 1 Winter is Coming gared It's …
15 2011/4/17 Season 1 Episode 1 Winter is Coming jon snow Go on. Father's watching.
16 2011/4/17 Season 1 Episode 1 Winter is Coming jon snow And your mother.
17 2011/4/17 Season 1 Episode 1 Winter is Coming septa mordane Fine work, as always. Well done.
18 2011/4/17 Season 1 Episode 1 Winter is Coming sansa stark Thank you.
19 2011/4/17 Season 1 Episode 1 Winter is Coming septa mordane I love the detail that you've managed to get i... 
data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23911 entries, 0 to 23910
Data columns (total 6 columns):#   Column         Non-Null Count  Dtype
---  ------         --------------  ----- 0   Release Date   23911 non-null  object1   Season         23911 non-null  object2   Episode        23911 non-null  object3   Episode Title  23911 non-null  object4   Name           23911 non-null  object5   Sentence       23911 non-null  object
dtypes: object(6)
memory usage: 1.1+ MB

data1['Name'].nunique()
#一共有564个人物

564

#针对单元格，使用describe，结果是tyrion lannister，次数是1760
data1['Name'].describe()

count                23911
unique                 564
top       tyrion lannister
freq                  1760
Name: Name, dtype: object

第三个题目不会

现有一份关于科比的投篮数据集，请解决如下问题：

  • 哪种action_type和combined_shot_type的组合是最多的？
  • 在所有被记录的game_id中，遭遇到最多的opponent是一个支？
data2=pd.read_csv('E:\jupyter Notebook\天池比赛\joyful-pandas-master\data\\Kobe_data.csv')
data2
shot_id action_type combined_shot_type game_event_id game_id lat loc_x loc_y lon minutes_remaining ... shot_made_flag shot_type shot_zone_area shot_zone_basic shot_zone_range team_id team_name game_date matchup opponent
0 1 Jump Shot Jump Shot 10 20000012 33.9723 167 72 -118.1028 10 ... NaN 2PT Field Goal Right Side(R) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
1 2 Jump Shot Jump Shot 12 20000012 34.0443 -157 0 -118.4268 10 ... 0.0 2PT Field Goal Left Side(L) Mid-Range 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
2 3 Jump Shot Jump Shot 35 20000012 33.9093 -101 135 -118.3708 7 ... 1.0 2PT Field Goal Left Side Center(LC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
3 4 Jump Shot Jump Shot 43 20000012 33.8693 138 175 -118.1318 6 ... 0.0 2PT Field Goal Right Side Center(RC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
4 5 Driving Dunk Shot Dunk 155 20000012 34.0443 0 0 -118.2698 6 ... 1.0 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
30692 30693 Jump Shot Jump Shot 397 49900088 33.9963 1 48 -118.2688 6 ... 0.0 2PT Field Goal Center(C) In The Paint (Non-RA) Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/6/19 LAL vs. IND IND
30693 30694 Tip Shot Tip Shot 398 49900088 34.0443 0 0 -118.2698 6 ... NaN 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/6/19 LAL vs. IND IND
30694 30695 Running Jump Shot Jump Shot 426 49900088 33.8783 -134 166 -118.4038 3 ... 1.0 2PT Field Goal Left Side Center(LC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/6/19 LAL vs. IND IND
30695 30696 Jump Shot Jump Shot 448 49900088 33.7773 31 267 -118.2388 2 ... 0.0 3PT Field Goal Center(C) Above the Break 3 24+ ft. 1610612747 Los Angeles Lakers 2000/6/19 LAL vs. IND IND
30696 30697 Jump Shot Jump Shot 471 49900088 33.9723 1 72 -118.2688 0 ... 0.0 2PT Field Goal Center(C) In The Paint (Non-RA) Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/6/19 LAL vs. IND IND

30697 rows × 25 columns

data2=data2.set_index('shot_id')

---------------------------------------------------------------------------KeyError                                  Traceback (most recent call last)<ipython-input-135-7f5596703652> in <module>
----> 1 data2=data2.set_index('shot_id')2 data2.head(20)C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)4301 4302         if missing:
-> 4303             raise KeyError(f"None of {missing} are in the columns")4304 4305         if inplace:KeyError: "None of ['shot_id'] are in the columns"

data2.head(20)
action_type combined_shot_type game_event_id game_id lat loc_x loc_y lon minutes_remaining period ... shot_made_flag shot_type shot_zone_area shot_zone_basic shot_zone_range team_id team_name game_date matchup opponent
shot_id
1 Jump Shot Jump Shot 10 20000012 33.9723 167 72 -118.1028 10 1 ... NaN 2PT Field Goal Right Side(R) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
2 Jump Shot Jump Shot 12 20000012 34.0443 -157 0 -118.4268 10 1 ... 0.0 2PT Field Goal Left Side(L) Mid-Range 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
3 Jump Shot Jump Shot 35 20000012 33.9093 -101 135 -118.3708 7 1 ... 1.0 2PT Field Goal Left Side Center(LC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
4 Jump Shot Jump Shot 43 20000012 33.8693 138 175 -118.1318 6 1 ... 0.0 2PT Field Goal Right Side Center(RC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
5 Driving Dunk Shot Dunk 155 20000012 34.0443 0 0 -118.2698 6 2 ... 1.0 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
6 Jump Shot Jump Shot 244 20000012 34.0553 -145 -11 -118.4148 9 3 ... 0.0 2PT Field Goal Left Side(L) Mid-Range 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
7 Layup Shot Layup 251 20000012 34.0443 0 0 -118.2698 8 3 ... 1.0 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
8 Jump Shot Jump Shot 254 20000012 34.0163 1 28 -118.2688 8 3 ... NaN 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
9 Jump Shot Jump Shot 265 20000012 33.9363 -65 108 -118.3348 6 3 ... 1.0 2PT Field Goal Left Side(L) In The Paint (Non-RA) 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
10 Running Jump Shot Jump Shot 294 20000012 33.9193 -33 125 -118.3028 3 3 ... 0.0 2PT Field Goal Center(C) In The Paint (Non-RA) 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
11 Jump Shot Jump Shot 309 20000012 33.8063 -94 238 -118.3638 1 3 ... 0.0 3PT Field Goal Left Side Center(LC) Above the Break 3 24+ ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR
12 Jump Shot Jump Shot 4 20000019 33.9173 121 127 -118.1488 11 1 ... 1.0 2PT Field Goal Right Side Center(RC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
13 Running Jump Shot Jump Shot 27 20000019 33.9343 -67 110 -118.3368 7 1 ... 1.0 2PT Field Goal Left Side(L) In The Paint (Non-RA) 8-16 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
14 Jump Shot Jump Shot 66 20000019 34.0403 -94 4 -118.3638 2 1 ... 0.0 2PT Field Goal Left Side(L) Mid-Range 8-16 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
15 Jump Shot Jump Shot 80 20000019 33.9973 -23 47 -118.2928 1 1 ... 0.0 2PT Field Goal Center(C) In The Paint (Non-RA) Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
16 Jump Shot Jump Shot 86 20000019 33.8523 62 192 -118.2078 0 1 ... 0.0 2PT Field Goal Center(C) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
17 Driving Layup Shot Layup 100 20000019 34.0443 0 0 -118.2698 0 1 ... NaN 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
18 Jump Shot Jump Shot 138 20000019 33.8183 -117 226 -118.3868 8 2 ... 1.0 3PT Field Goal Left Side Center(LC) Above the Break 3 24+ ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
19 Jump Shot Jump Shot 244 20000019 33.9473 -132 97 -118.4018 11 3 ... 0.0 2PT Field Goal Left Side Center(LC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA
20 Driving Layup Shot Layup 249 20000019 34.0443 0 0 -118.2698 10 3 ... NaN 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/11/1 LAL vs. UTA UTA

20 rows × 24 columns

data2.groupby(by=('action_type','combined_shot_type'))

---------------------------------------------------------------------------KeyError                                  Traceback (most recent call last)<ipython-input-138-e68698f5f785> in <module>
----> 1 data2.groupby(by=('action_type','combined_shot_type'))C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed)5808             group_keys=group_keys,5809             squeeze=squeeze,
-> 5810             observed=observed,5811         )5812 C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated)407                 sort=sort,408                 observed=observed,
--> 409                 mutated=self.mutated,410             )411 C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\grouper.py in get_grouper(obj, key, axis, level, sort, observed, mutated, validate)596                 in_axis, name, level, gpr = False, None, gpr, None597             else:
--> 598                 raise KeyError(gpr)599         elif isinstance(gpr, Grouper) and gpr.key is not None:600             # Add key to exclusionsKeyError: ('action_type', 'combined_shot_type')

data2_join=data2.assign(join=data2['combined_shot_type']+data2['action_type'])
data2_join.head()
action_type combined_shot_type game_event_id game_id lat loc_x loc_y lon minutes_remaining period ... shot_type shot_zone_area shot_zone_basic shot_zone_range team_id team_name game_date matchup opponent join
shot_id
1 Jump Shot Jump Shot 10 20000012 33.9723 167 72 -118.1028 10 1 ... 2PT Field Goal Right Side(R) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR Jump ShotJump Shot
2 Jump Shot Jump Shot 12 20000012 34.0443 -157 0 -118.4268 10 1 ... 2PT Field Goal Left Side(L) Mid-Range 8-16 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR Jump ShotJump Shot
3 Jump Shot Jump Shot 35 20000012 33.9093 -101 135 -118.3708 7 1 ... 2PT Field Goal Left Side Center(LC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR Jump ShotJump Shot
4 Jump Shot Jump Shot 43 20000012 33.8693 138 175 -118.1318 6 1 ... 2PT Field Goal Right Side Center(RC) Mid-Range 16-24 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR Jump ShotJump Shot
5 Driving Dunk Shot Dunk 155 20000012 34.0443 0 0 -118.2698 6 2 ... 2PT Field Goal Center(C) Restricted Area Less Than 8 ft. 1610612747 Los Angeles Lakers 2000/10/31 LAL @ POR POR DunkDriving Dunk Shot

5 rows × 25 columns

data2_join1=data2['combined_shot_type']+'+'+data2['action_type']

data2_join1.head(100)

shot_id
1         Jump Shot+Jump Shot
2         Jump Shot+Jump Shot
3         Jump Shot+Jump Shot
4         Jump Shot+Jump Shot
5      Dunk+Driving Dunk Shot...
96        Jump Shot+Jump Shot
97        Jump Shot+Jump Shot
98        Jump Shot+Jump Shot
99        Jump Shot+Jump Shot
100       Jump Shot+Jump Shot
Length: 100, dtype: object

data2_join1.nunique()

57

data2_join1.value_counts()
#最多的是Jump Shot+Jump Shot

Jump Shot+Jump Shot                             18880
Layup+Layup Shot                                 2567
Layup+Driving Layup Shot                         1978
Jump Shot+Turnaround Jump Shot                   1057
Jump Shot+Fadeaway Jump Shot                     1048
Jump Shot+Running Jump Shot                       926
Jump Shot+Pullup Jump shot                        476
Jump Shot+Turnaround Fadeaway shot                439
Dunk+Slam Dunk Shot                               411
Layup+Reverse Layup Shot                          395
Jump Shot+Jump Bank Shot                          333
Dunk+Driving Dunk Shot                            310
Dunk+Dunk Shot                                    262
Tip Shot+Tip Shot                                 182
Dunk+Alley Oop Dunk Shot                          122
Jump Shot+Step Back Jump shot                     118
Jump Shot+Floating Jump shot                      114
Layup+Driving Reverse Layup Shot                   97
Hook Shot+Hook Shot                                84
Layup+Driving Finger Roll Shot                     82
Layup+Alley Oop Layup shot                         80
Dunk+Reverse Dunk Shot                             75
Layup+Running Layup Shot                           72
Bank Shot+Turnaround Bank shot                     71
Layup+Driving Finger Roll Layup Shot               69
Bank Shot+Running Bank shot                        48
Dunk+Driving Slam Dunk Shot                        48
Hook Shot+Running Hook Shot                        41
Layup+Finger Roll Layup Shot                       33
Jump Shot+Fadeaway Bank shot                       31
Layup+Finger Roll Shot                             28
Jump Shot+Driving Jump shot                        28
Jump Shot+Jump Hook Shot                           24
Dunk+Running Dunk Shot                             19
Dunk+Reverse Slam Dunk Shot                        16
Layup+Putback Layup Shot                           15
Dunk+Follow Up Dunk Shot                           15
Hook Shot+Turnaround Hook Shot                     14
Hook Shot+Driving Hook Shot                        14
Bank Shot+Pullup Bank shot                         12
Layup+Running Reverse Layup Shot                   11
Layup+Running Finger Roll Layup Shot                6
Layup+Cutting Layup Shot                            6
Bank Shot+Driving Bank shot                         5
Bank Shot+Hook Bank Shot                            5
Dunk+Putback Dunk Shot                              5
Jump Shot+Driving Floating Jump Shot                5
Layup+Running Finger Roll Shot                      4
Jump Shot+Running Pull-Up Jump Shot                 4
Layup+Turnaround Finger Roll Shot                   2
Tip Shot+Running Tip Shot                           2
Layup+Tip Layup Shot                                2
Dunk+Putback Slam Dunk Shot                         2
Dunk+Running Slam Dunk Shot                         1
Jump Shot+Turnaround Fadeaway Bank Jump Shot        1
Jump Shot+Driving Floating Bank Jump Shot           1
Layup+Cutting Finger Roll Layup Shot                1
dtype: int64

data2['opponent'].value_counts()
#遭遇最多的是SAS

SAS    1978
PHX    1781
HOU    1666
SAC    1643
DEN    1642
POR    1539
UTA    1490
MIN    1474
GSW    1356
LAC    1285
DAL    1199
MEM    1030
BOS     946
SEA     828
IND     761
PHI     720
ORL     719
DET     715
OKC     677
TOR     664
NYK     657
MIA     627
CHA     620
CLE     619
CHI     610
WAS     600
MIL     586
NOH     581
NJN     520
ATL     519
NOP     344
VAN     246
BKN      55
Name: opponent, dtype: int64

经验总结

本次是第二次参加组队学习，这次是针对pandas的学习
在这个练习中暂时没有遇到什么问题，除了在课后习题中第一题的第三问做不出来，之后等学习到那个时候在解决。其次是自己的解决方案和参考答案方法不同，但结果相同

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