Pandas操作题

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Pandas操作题

1.学生数据分析

1.数据导入并展示, 文件位置位于../data/下

• 请将文件student-info.csv中的内容读入pandas DataFrame "df_info"，注意该文件的分隔符是分号，并展示前五行
• 请将文件student-score.csv中的内容读入pandas DataFrame "df_score"，注意该文件的分隔符是分号，并展示前五行

In [1]:

 

import pandas as pd

df_info = pd.read_csv("C:/Users/PycharmProjects/pythonProject/ML/Projects/课程/资料/homework/data/student-info.csv",sep = ';')

df_info.head(5)

Out[1]:

	ID	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	...	higher	internet	romantic	famrel	freetime	goout	Dalc	Walc	health	absences
0	0	GP	F	18	U	GT3	A	4	4	at_home	...	yes	no	no	4	3	4	1	1	3	6
1	1	GP	F	17	U	GT3	T	1	1	at_home	...	yes	yes	no	5	3	3	1	1	3	4
2	2	GP	F	15	U	LE3	T	1	1	at_home	...	yes	yes	no	4	3	2	2	3	3	10
3	3	GP	F	15	U	GT3	T	4	2	health	...	yes	yes	yes	3	2	2	1	1	5	2
4	4	GP	F	16	U	GT3	T	3	3	other	...	yes	no	no	4	3	2	1	2	5	4

5 rows × 31 columns

In [3]:

 

import pandas as pd

df_score = pd.read_csv("C:/Users/PycharmProjects/pythonProject/ML/Projects/课程/资料/homework/data/student-score.csv",sep = ';')

df_score.head(5)

Out[3]:

	ID	G1	G2	G3
0	0	5	6	6
1	1	5	5	6
2	2	7	8	10
3	3	15	14	15
4	4	6	10	10

2.把两个DataFrame "df_info" 和 "df_score" 按照学生ID对齐拼接起来，结果存在一个新DataFrame "df" 中

In [55]:

 

# df = pd.concat([df_info,df_score], axis=1)

# df_info.merge(df_score)

df = pd.merge(df_info,df_score,on=['ID'])

​

3.把"df"所有的列展示出来

In [56]:

 

df

Out[56]:

	ID	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	...	famrel	freetime	goout	Dalc	Walc	health	absences	G1	G2	G3
0	0	GP	F	18	U	GT3	A	4	4	at_home	...	4	3	4	1	1	3	6	5	6	6
1	1	GP	F	17	U	GT3	T	1	1	at_home	...	5	3	3	1	1	3	4	5	5	6
2	2	GP	F	15	U	LE3	T	1	1	at_home	...	4	3	2	2	3	3	10	7	8	10
3	3	GP	F	15	U	GT3	T	4	2	health	...	3	2	2	1	1	5	2	15	14	15
4	4	GP	F	16	U	GT3	T	3	3	other	...	4	3	2	1	2	5	4	6	10	10
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
390	390	MS	M	20	U	LE3	A	2	2	services	...	5	5	4	4	5	4	11	9	9	9
391	391	MS	M	17	U	LE3	T	3	1	services	...	2	4	5	3	4	2	3	14	16	16
392	392	MS	M	21	R	GT3	T	1	1	other	...	5	5	3	3	3	3	3	10	8	7
393	393	MS	M	18	R	LE3	T	3	2	services	...	4	4	1	3	4	5	0	11	12	10
394	394	MS	M	19	U	LE3	T	1	1	other	...	3	2	3	3	3	5	5	8	9	9

395 rows × 34 columns

In [16]:

 

df.columns

Out[16]:

Index(['ID', 'school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu','Fedu', 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime','failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery','higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc','Walc', 'health', 'absences', 'ID', 'G1', 'G2', 'G3'],dtype='object')

4.统计男生的平均分和女生的平均分(G1, G2, G3)，使用groupby和aggregate来操作

In [59]:

 

group_by_sex = df.groupby('sex').agg('mean')

group_by_sex[['G1','G2','G3']]

#group_by_sex = df.groupby('sex').mean()

#group_by_sex[['G1','G2','G3']]

Out[59]:

	G1	G2	G3
sex
F	10.620192	10.389423	9.966346
M	11.229947	11.074866	10.914439

In [60]:

 

group_by_agg = group_by_sex.aggregate('mean')

group_by_agg[['G1','G2','G3']]

Out[60]:

G1    10.925069
G2    10.732145
G3    10.440392
dtype: float64

5.统计不同年龄的学生(G1, G2, G3)的平均分，并作出柱状图展示

In [70]:

 

group_by_age = df.groupby('age').mean()

#group_by_age[['G1','G2','G3']]

group_by_age[['G1','G2','G3']].plot(kind='bar')

Out[70]:

<AxesSubplot:xlabel='age'>

6.统计不同学校(school一列)的学生的平均分，并作柱状图展示，要求同一科目的两个学校成绩贴在一起展示

In [88]:

 

group_by_school=df.groupby('school').agg('mean').transpose()

group_by_school.plot(kind='bar')

#roup_by_school.T.plot(kind='bar')

​

Out[88]:

<AxesSubplot:>

7.统计一下每个学校三个科目总分的前十名，然后把前十名的同学所有信息输出

In [201]:

 

#group_by_school=df.groupby(['school']).agg('sum').transpose()

# group_by_school['G1','G2','G3']

​

df["total"] = df["G1"] + df["G2"]+ df["G3"]

df.groupb

​

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-201-88778c814a96> in <module>3 4 df["total"] = df["G1"] + df["G2"]+ df["G3"]
----> 5 df.groupby['school']6 # df.groupby(['school','total'])7 TypeError: 'method' object is not subscriptable

2.沃尔玛销售数据整理

1. 数据导入并展示, 文件位置位于 ../data下

• 请将文件 stores.csv 中的内容读入pandas DataFrame "df_stores"，并展示前五行以及打印df_stores的shape

In [211]:

 

import pandas as pd

df_stores = pd.read_csv(r"C:\Users\PycharmProjects\pythonProject\ML\Projects\课程\资料\homework\data\walmart\walmart\stores.csv")

df_stores.head(5)

​

Out[211]:

	Store	Type	Size
0	1	A	151315
1	2	A	202307
2	3	B	37392
3	4	A	205863
4	5	B	34875

In [212]:

 

df_stores.shape

Out[212]:

(45, 3)

• 请将文件 features.csv 中的内容读入pandas DataFrame "df_feats"，将df_feats改成只留下以下几个columns: "Store", "Date", "Temperature", "Fuel_Price", "CPI", "Unemployment", 并展示前五行以及打印df_feats的shape

In [215]:

 

df_feats = pd.read_csv(r"C:\Users\PycharmProjects\pythonProject\ML\Projects\课程\资料\homework\data\walmart\walmart\features.csv")

df_feats.loc[:,["Store", "Date", "Temperature", "Fuel_Price", "CPI", "Unemployment"]].head()

Out[215]:

	Store	Date	Temperature	Fuel_Price	CPI	Unemployment
0	1	2010-02-05	42.31	2.572	211.096358	8.106
1	1	2010-02-12	38.51	2.548	211.242170	8.106
2	1	2010-02-19	39.93	2.514	211.289143	8.106
3	1	2010-02-26	46.63	2.561	211.319643	8.106
4	1	2010-03-05	46.50	2.625	211.350143	8.106

In [217]:

 

df_feats.shape

Out[217]:

(8190, 12)

• 请将文件 sales.csv 中的内容读入pandas DataFrame "df_sales"，并展示前五行以及打印df_sales的shape

In [219]:

 

df_sales = pd.read_csv(r"C:\Users\PycharmProjects\pythonProject\ML\Projects\课程\资料\homework\data\walmart\walmart\sales.csv")

df_sales.head(5)

Out[219]:

	Store	Dept	Date	Weekly_Sales	IsHoliday
0	1	1	2010-02-05	24924.50	False
1	1	1	2010-02-12	46039.49	True
2	1	1	2010-02-19	41595.55	False
3	1	1	2010-02-26	19403.54	False
4	1	1	2010-03-05	21827.90	False

In [220]:

 

df_sales.shape

Out[220]:

(421570, 5)

8. 我们发现 df_sales 中每个Store有很多个Dept(department)， 我们只关心整家店的销售情况，所以请把这张表格按照Store和Date来计算整店销售总额，IsHoliday这一列不必保留。结果保存在df_sales_by_store中

In [257]:

 

df_sales_by_store=df_sales.loc[:,["Store", "Dept", "Date", "Weekly_Sales"]].groupby(['Store','Date']).sum()

df_sales_by_store

​

Out[257]:

		Dept	Weekly_Sales
Store	Date
1	2010-02-05	3184	1643690.90
	2010-02-12	3137	1641957.44
	2010-02-19	3184	1611968.17
	2010-02-26	3137	1409727.59
	2010-03-05	3215	1554806.68
...	...	...	...
45	2012-09-28	2853	713173.95
	2012-10-05	2952	733455.07
	2012-10-12	2853	734464.36
	2012-10-19	2853	718125.53
	2012-10-26	2907	760281.43

6435 rows × 2 columns

9. 使用 Store 和 Date 这两列合并 df_sales_by_store 和 df_feats 两张表格，结果保存在 df_sale_feats 。

In [261]:

 

df_sale_feats = pd.merge(df_sales_by_store,df_feats,on=['Store','Date'])

df_sale_feats

Out[261]:

	Store	Date	Dept	Weekly_Sales	Temperature	Fuel_Price	MarkDown1	MarkDown2	MarkDown3	MarkDown4	MarkDown5	CPI	Unemployment	IsHoliday
0	1	2010-02-05	3184	1643690.90	42.31	2.572	NaN	NaN	NaN	NaN	NaN	211.096358	8.106	False
1	1	2010-02-12	3137	1641957.44	38.51	2.548	NaN	NaN	NaN	NaN	NaN	211.242170	8.106	True
2	1	2010-02-19	3184	1611968.17	39.93	2.514	NaN	NaN	NaN	NaN	NaN	211.289143	8.106	False
3	1	2010-02-26	3137	1409727.59	46.63	2.561	NaN	NaN	NaN	NaN	NaN	211.319643	8.106	False
4	1	2010-03-05	3215	1554806.68	46.50	2.625	NaN	NaN	NaN	NaN	NaN	211.350143	8.106	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6430	45	2012-09-28	2853	713173.95	64.88	3.997	4556.61	20.64	1.50	1601.01	3288.25	192.013558	8.684	False
6431	45	2012-10-05	2952	733455.07	64.89	3.985	5046.74	NaN	18.82	2253.43	2340.01	192.170412	8.667	False
6432	45	2012-10-12	2853	734464.36	54.47	4.000	1956.28	NaN	7.89	599.32	3990.54	192.327265	8.667	False
6433	45	2012-10-19	2853	718125.53	56.47	3.969	2004.02	NaN	3.18	437.73	1537.49	192.330854	8.667	False
6434	45	2012-10-26	2907	760281.43	58.85	3.882	4018.91	58.08	100.00	211.94	858.33	192.308899	8.667	False

6435 rows × 14 columns

10. 使用 Store 这一列合并 df_sale_feats 和 df_stores 两张表格，结果仍然保留在df_sale_feats 中。

In [266]:

 

df_sale_feats = pd.merge(df_sale_feats,df_stores,on=['Store'])

df_sale_feats

Out[266]:

	Store	Date	Dept	Weekly_Sales	Temperature	Fuel_Price	MarkDown1	MarkDown2	MarkDown3	MarkDown4	MarkDown5	CPI	Unemployment	IsHoliday	Type	Size
0	1	2010-02-05	3184	1643690.90	42.31	2.572	NaN	NaN	NaN	NaN	NaN	211.096358	8.106	False	A	151315
1	1	2010-02-12	3137	1641957.44	38.51	2.548	NaN	NaN	NaN	NaN	NaN	211.242170	8.106	True	A	151315
2	1	2010-02-19	3184	1611968.17	39.93	2.514	NaN	NaN	NaN	NaN	NaN	211.289143	8.106	False	A	151315
3	1	2010-02-26	3137	1409727.59	46.63	2.561	NaN	NaN	NaN	NaN	NaN	211.319643	8.106	False	A	151315
4	1	2010-03-05	3215	1554806.68	46.50	2.625	NaN	NaN	NaN	NaN	NaN	211.350143	8.106	False	A	151315
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6430	45	2012-09-28	2853	713173.95	64.88	3.997	4556.61	20.64	1.50	1601.01	3288.25	192.013558	8.684	False	B	118221
6431	45	2012-10-05	2952	733455.07	64.89	3.985	5046.74	NaN	18.82	2253.43	2340.01	192.170412	8.667	False	B	118221
6432	45	2012-10-12	2853	734464.36	54.47	4.000	1956.28	NaN	7.89	599.32	3990.54	192.327265	8.667	False	B	118221
6433	45	2012-10-19	2853	718125.53	56.47	3.969	2004.02	NaN	3.18	437.73	1537.49	192.330854	8.667	False	B	118221
6434	45	2012-10-26	2907	760281.43	58.85	3.882	4018.91	58.08	100.00	211.94	858.33	192.308899	8.667	False	B	118221

6435 rows × 16 columns

In [ ]:

 

​

 

​

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