Python数据分析pandas入门练习题

Posted 2022-10-25 Geek_bao

Python数据分析基础

• Preparation
• Exercise 1- US - Baby Names
• • • Introduction:
    • Step 1. Import the necessary libraries
    • Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv).
    • Step 3. Assign it to a variable called baby_names.
    • Step 4. See the first 10 entries
    • Step 5. Delete the column 'Unnamed: 0' and 'Id'
    • Step 6. Is there more male or female names in the dataset?
    • Step 7. Group the dataset by name and assign to names
    • Step 8. How many different names exist in the dataset?
    • Step 9. What is the name with most occurrences?
    • Step 10. How many different names have the least occurrences?
    • Step 11. What is the median name occurrence?
    • Step 12. What is the standard deviation of names?
    • Step 13. Get a summary with the mean, min, max, std and quartiles.
• Exercise 2- Wind Statistics
• • • Introduction:
    • Step 1. Import the necessary libraries
    • Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/Wind_Stats/wind.data)
    • Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index.
    • Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it.
    • Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns].
    • Step 6. Compute how many values are missing for each location over the entire record.
    • • They should be ignored in all calculations below.
    • Step 7. Compute how many non-missing values there are in total.
    • Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.
    • • A single number for the entire dataset.
    • Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days
    • • A different set of numbers for each location.
    • Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.
    • • A different set of numbers for each day.
    • Step 11. Find the average windspeed in January for each location.
    • • Treat January 1961 and January 1962 both as January.
    • Step 12. Downsample the record to a yearly frequency for each location.
    • Step 13. Downsample the record to a monthly frequency for each location.
    • Step 14. Downsample the record to a weekly frequency for each location.
    • Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks.
• Conclusion

Preparation

需要数据集可以自行网上寻找（都是公开的数据集）或私聊博主，传到csdn，你们下载要会员，就不传了。下面数据集链接下载不一定能成功。

Exercise 1- US - Baby Names

Introduction:

We are going to use a subset of US Baby Names from Kaggle.
In the file it will be names from 2004 until 2014

Step 1. Import the necessary libraries

代码如下：

import pandas as pd

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called baby_names.

代码如下：

baby_names = pd.read_csv("US_Baby_Names_right.csv")
baby_names.info()

输出结果如下：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1016395 entries, 0 to 1016394
Data columns (total 7 columns):
Unnamed: 0    1016395 non-null int64
Id            1016395 non-null int64
Name          1016395 non-null object
Year          1016395 non-null int64
Gender        1016395 non-null object
State         1016395 non-null object
Count         1016395 non-null int64
dtypes: int64(4), object(3)
memory usage: 54.3+ MB

Step 4. See the first 10 entries

代码如下：

baby_names.head(10)

输出结果如下：

	Unnamed: 0	Id	Name	Year	Gender	State	Count
0	11349	11350	Emma	2004	F	AK	62
1	11350	11351	Madison	2004	F	AK	48
2	11351	11352	Hannah	2004	F	AK	46
3	11352	11353	Grace	2004	F	AK	44
4	11353	11354	Emily	2004	F	AK	41
5	11354	11355	Abigail	2004	F	AK	37
6	11355	11356	Olivia	2004	F	AK	33
7	11356	11357	Isabella	2004	F	AK	30
8	11357	11358	Alyssa	2004	F	AK	29
9	11358	11359	Sophia	2004	F	AK	28

Step 5. Delete the column ‘Unnamed: 0’ and ‘Id’

代码如下：

del baby_names['Id']
# OR del baby_names['Unnamed: 0']
baby_names = baby_names.loc[:, ~baby_names.columns.str.contains('^Unnamed')]
baby_names.head()

输出结果如下：

	Name	Year	Gender	State	Count
0	Emma	2004	F	AK	62
1	Madison	2004	F	AK	48
2	Hannah	2004	F	AK	46
3	Grace	2004	F	AK	44
4	Emily	2004	F	AK	41

Step 6. Is there more male or female names in the dataset?

代码如下：

# baby_names['Gender'].value_counts()
baby_names.groupby('Gender').Count.sum()

输出结果如下：

Gender
F    16380293
M    19041199
Name: Count, dtype: int64

Step 7. Group the dataset by name and assign to names

代码如下：

del baby_names["Year"]
names = baby_names.groupby("Name").sum()
names.head()
print(names.shape)
names.sort_values("Count", ascending = 0).head()
# names= baby_names.groupby('Name')
# names.head(1)

输出结果如下：

(17632, 1)

	Count
Name
Jacob	242874
Emma	214852
Michael	214405
Ethan	209277
Isabella	204798

Step 8. How many different names exist in the dataset?

代码如下：

len(names)

输出结果如下：

17632

Step 9. What is the name with most occurrences?

代码如下：

# names['Count'].sum().argmax()
names.Count.idxmax()  # idxmax()获取pandas中series最大值对应的索引

输出结果如下：

'Jacob'

Step 10. How many different names have the least occurrences?

代码如下：

len(names[names.Count == names.Count.min()])

输出结果如下：

2578

Step 11. What is the median name occurrence?

代码如下：

names[names.Count == names.Count.median()]

输出结果如下：

	Count
Name
Aishani	49
Alara	49
Alysse	49
Ameir	49
Anely	49
Antonina	49
Aveline	49
Aziah	49
Baily	49
Caleah	49
Carlota	49
Cristine	49
Dahlila	49
Darvin	49
Deante	49
Deserae	49
Devean	49
Elizah	49
Emmaly	49
Emmanuela	49
Envy	49
Esli	49
Fay	49
Gurshaan	49
Hareem	49
Iven	49
Jaice	49
Jaiyana	49
Jamiracle	49
Jelissa	49
...	...
Kyndle	49
Kynsley	49
Leylanie	49
Maisha	49
Malillany	49
Mariann	49
Marquell	49
Maurilio	49
Mckynzie	49
Mehdi	49
Nabeel	49
Nalleli	49
Nassir	49
Nazier	49
Nishant	49
Rebecka	49
Reghan	49
Ridwan	49
Riot	49
Rubin	49
Ryatt	49
Sameera	49
Sanjuanita	49
Shalyn	49
Skylie	49
Sriram	49
Trinton	49
Vita	49
Yoni	49
Zuleima	49

66 rows × 1 columns

Step 12. What is the standard deviation of names?

代码如下：

names.Count.std()

输出结果如下：

11006.069467891111

Step 13. Get a summary with the mean, min, max, std and quartiles.

代码如下：

names.describe()

输出结果如下：

	Count
count	17632.000000
mean	2008.932169
std	11006.069468
min	5.000000
25%	11.000000
50%	49.000000
75%	337.000000
max	242874.000000

Exercise 2- Wind Statistics

Introduction:

The data have been modified to contain some missing values, identified by NaN.
Using pandas should make this exercise
easier, in particular for the bonus question.

You should be able to perform all of these operations without using
a for loop or other looping construct.

1. The data in ‘wind.data’ has the following format:

"""
Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL
61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04
61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83
61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71
"""

'\\nYr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\\n61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\\n61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\\n'

The first three columns are year, month and day. The
remaining 12 columns are average windspeeds in knots at 12
locations in Ireland on that day.

More information about the dataset go here.

Step 1. Import the necessary libraries

代码如下：

import pandas as pd
import datetime

Step 2. Import the dataset from this address

Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index.

代码如下：

data = pd.read_table('wind.data', sep='\\s+', parse_dates = [[0, 1, 2]])
data.head()

输出结果如下：

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	2061-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	2061-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	2061-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	2061-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	2061-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it.

代码如下：

def fix_century(x):
    year = x.year - 100 if x.year > 1989 else x.year
    return datetime.date(year, x.month, x.day)
data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)
data.head()

输出结果如下：

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns].

代码如下：

data["Yr_Mo_Dy"] = pd.to_datetime(data["Yr_Mo_Dy"])  # 转换为datetime64
data = data.set_index('Yr_Mo_Dy')
data.head()

输出结果如下：

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

Step 6. Compute how many values are missing for each location over the entire record.

They should be ignored in all calculations below.

代码如下：

data.isnull().sum()

输出结果如下：

RPT    6
VAL    3
ROS    2
KIL    5
SHA    2
BIR    0
DUB    3
CLA    2
MUL    3
CLO    1
BEL    0
MAL    4
dtype: int64

Step 7. Compute how many non-missing values there are in total.

代码如下：

data.shape[0] - data.isnull().sum()
#OR data.notnull.sum()

输出结果如下：

RPT    6568
VAL    6571
ROS    6572
KIL    6569
SHA    6572
BIR    6574
DUB    6571
CLA    6572
MUL    6571
CLO    6573
BEL    6574
MAL    6570
dtype: int64

Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.

A single number for the entire dataset.

代码如下：

data.fillna(0).values.flatten().mean()  # a.flatten()就是把data降到一维，默认是按行的方向降

输出结果如下：

10.223864592840483

Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days

A different set of numbers for each location.

代码如下：

# loc_stats = data.loc[:, 'RPT':'MAL'].describe(percentiles=[])
# loc_stats
data.describe(percentiles=[])

输出结果如下：

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
count	6568.000000	6571.000000	6572.000000	6569.000000	6572.000000	6574.000000	6571.000000	6572.000000	6571.000000	6573.000000	6574.000000	6570.000000
mean	12.362987	10.644314	11.660526	6.306468	10.455834	7.092254	9.797343	8.495053	8.493590	8.707332	13.121007	15.599079
std	5.618413	5.267356	5.008450	3.605811	4.936125	3.968683	4.977555	4.499449	4.166872	4.503954	5.835037	6.699794
min	0.670000	0.210000	1.500000	0.000000	0.130000	0.000000	0.000000	0.000000	0.000000	0.040000	0.130000	0.670000
50%	11.710000	10.170000	10.920000	5.750000	9.960000	6.830000	9.210000	8.080000	8.170000	8.290000	12.500000	15.000000
max	35.800000	33.370000	33.840000	28.460000	37.540000	26.160000	30.370000	31.080000	25.880000	28.210000	42.380000	42.540000

Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.

A different set of numbers for each day.

代码如下：

day_stats = pd.DataFrame()

day_stats['min'] = data.min(axis = 1)
day_stats['max'] = data.max(axis = 1)
day_stats['mean'] = data.mean(axis = 1)
day_stats['std'] = data.std(axis = 1)

day_stats.head()

输出结果如下：

	min	max	mean	std
Yr_Mo_Dy
1961-01-01	9.29	18.50	13.018182	2.808875
1961-01-02	6.50	17.54	11.336364	3.188994
1961-01-03	6.17	18.50	11.641818	3.681912
1961-01-04	1.79	11.75	6.619167	3.198126
1961-01-05	6.17	13.33	10.630000	2.445356

Step 11. Find the average windspeed in January for each location.

Treat January 1961 and January 1962 both as January.

代码如下：

data.loc[data.index.month == 1].mean()

输出结果如下：

RPT    14.847325
VAL    12.914560
ROS    13.299624
KIL     7.199498
SHA    11.667734
BIR     8.054839
DUB    11.819355
CLA     9.512047
MUL     9.543208
CLO    10.053566
BEL    14.550520
MAL    18.028763
dtype: float64

Step 12. Downsample the record to a yearly frequency for each location.

代码如下：

# pd.Period()创建时期数据
# pd.Period()参数：一个时间戳 + freq 参数 → freq 用于指明该 period 的长度，时间戳则说明该 period 在时间轴上的位置
# DatetimeIndex对象的数据转换为PeriodIndex
data.groupby(data.index.to_period('A')).mean() 

输出结果如下：

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1961	12.299583	10.351796	11.362369	6.958227	10.881763	7.729726	9.733923	8.858788	8.647652	9.835577	13.502795	13.680773
1962	12.246923	10.110438	11.732712	6.960440	10.657918	7.393068	11.020712	8.793753	8.316822	9.676247	12.930685	14.323956
1963	12.813452	10.836986	12.541151	7.330055	11.724110	8.434712	11.075699	10.336548	8.903589	10.224438	13.638877	14.999014
1964	12.363661	10.920164	12.104372	6.787787	11.454481	7.570874	10.259153	9.467350	7.789016	10.207951	13.740546	14.910301
1965	12.451370	11.075534	11.848767	6.858466	11.024795	7.478110	10.618712	8.879918	7.907425	9.918082	12.964247	15.591644
1966	13.461973	11.557205	12.020630	7.345726	11.805041	7.793671	10.579808	8.835096	8.514438	9.768959	14.265836	16.307260
1967	12.737151	10.990986	11.739397	7.143425	11.630740	7.368164	10.652027	9.325616	8.645014	9.547425	14.774548	17.135945
1968	11.835628	10.468197	11.409754	6.477678	10.760765	6.067322	8.859180	8.255519	7.224945	7.832978	12.808634	15.017486
1969	11.166356	9.723699	10.902000	5.767973	9.873918	6.189973	8.564493	7.711397	7.924521	7.754384	12.621233	15.762904
1970	12.600329	10.726932	11.730247	6.217178	10.567370	7.609452	9.609890	8.334630	9.297616	8.289808	13.183644	16.456027
1971	11.273123	9.095178	11.088329	5.241507	9.440329	6.097151	8.385890	6.757315	7.915370	7.229753	12.208932	15.025233
1972	12.463962	10.561311	12.058333	5.929699	9.430410	6.358825	9.704508	7.680792	8.357295	7.515273	12.727377	15.028716
1973	11.828466	10.680493	10.680493	5.547863	9.640877	6.548740	8.482110	7.614274	8.245534	7.812411	12.169699	15.441096
1974	13.643096	11.811781	12.336356	6.427041	11.110986	6.809781	10.084603	9.896986	9.331753	8.736356	13.252959	16.947671
1975	12.008575	10.293836	11.564712	5.269096	9.190082	5.668521	8.562603	7.843836	8.797945	7.382822	12.631671	15.307863
1976	11.737842	10.203115	10.761230	5.109426	8.846339	6.311038	9.149126	7.146202	8.883716	7.883087	12.332377	15.471448
1977	13.099616	11.144493	12.627836	6.073945	10.003836	8.586438	11.523205	8.378384	9.098192	8.821616	13.459068	16.590849
1978	12.504356	11.044274	11.380000	6.082356	10.167233	7.650658	9.489342	8.800466	9.089753	8.301699	12.967397	16.771370

Step 13. Downsample the record to a monthly frequency for each location.

代码如下：

data.groupby(data.index.to_period('M')).mean().head()

输出结果如下：

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1961-01	14.841333	11.988333	13.431613	7.736774	11.072759	8.588065	11.184839	9.245333	9.085806	10.107419	13.880968	14.703226
1961-02	16.269286	14.975357	14.441481	9.230741	13.852143	10.937500	11.890714	11.846071	11.821429	12.714286	18.583214	15.411786
1961-03	10.890000	11.296452	10.752903	7.284000	10.509355	8.866774	9.644194	9.829677	10.294138	11.251935	16.410968	15.720000
1961-04	10.722667	9.427667	9.998000	5.830667	8.435000	6.495000	6.925333	7.094667	7.342333	7.237000	11.147333	10.278333
1961-05	9.860968	8.850000	10.818065	5.905333	9.490323	6.574839	7.604000	8.177097	8.039355	8.499355	11.900323	12.011613

Step 14. Downsample the record to a weekly frequency for each location.

代码如下：

data.groupby(data.index.to_period('W')).mean().head()

输出结果如下：

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1960-12-26/1961-01-01	15.040000	14.960000	13.170000	9.290000	NaN	9.870000	13.670000	10.250000	10.830000	12.580000	18.500000	15.040000
1961-01-02/1961-01-08	13.541429	11.486667	10.487143	6.417143	9.474286	6.435714	11.061429	6.616667	8.434286	8.497143	12.481429	13.238571
1961-01-09/1961-01-15	12.468571	8.967143	11.958571	4.630000	7.351429	5.072857	7.535714	6.820000	5.712857	7.571429	11.125714	11.024286
1961-01-16/1961-01-22	13.204286	9.862857	12.982857	6.328571	8.966667	7.417143	9.257143	7.875714	7.145714	8.124286	9.821429	11.434286
1961-01-23/1961-01-29	19.880000	16.141429	18.225714	12.720000	17.432857	14.828571	15.528571	15.160000	14.480000	15.640000	20.930000	22.530000

Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks.

代码如下：

# data.groupby(data.index.to_period('1961-01-02', 'W')).describe(percentiles=[]).head()
weekly = data.resample('W').agg(['min', 'max', 'mean', 'std'])  # resample()重新设置频率采样，再sh
weekly.loc[weekly.index[1:53], "RPT":"MAL"].head(10)

输出结果如下：

	RPT				VAL				ROS		...	CLO		BEL				MAL
	min	max	mean	std	min	max	mean	std	min	max	...	mean	std	min	max	mean	std	min	max	mean	std
Yr_Mo_Dy
1961-01-08	10.58	18.50	13.541429	2.631321	6.63	16.88	11.486667	3.949525	7.62	12.33	...	8.497143	1.704941	5.46	17.54	12.481429	4.349139	10.88	16.46	13.238571	1.773062
1961-01-15	9.04	19.75	12.468571	3.555392	3.54	12.08	8.967143	3.148945	7.08	19.50	...	7.571429	4.084293	5.25	20.71	11.125714	5.552215	5.17	16.92	11.024286	4.692355
1961-01-22	4.92	19.83	13.204286	5.337402	3.42	14.37	9.862857	3.837785	7.29	20.79	...	8.124286	4.783952	6.50	15.92	9.821429	3.626584	6.79	17.96	11.434286	4.237239
1961-01-29	13.62	25.04	19.880000	4.619061	9.96	23.91	16.141429	5.170224	12.67	25.84	...	15.640000	3.713368	14.04	27.71	20.930000	5.210726	17.50	27.63	22.530000	3.874721
1961-02-05	10.58	24.21	16.827143	5.251408	9.46	24.21	15.460000	5.187395	9.04	19.70	...	9.460000	2.839501	9.17	19.33	14.012857	4.210858	7.17	19.25	11.935714	4.336104
1961-02-12	16.00	24.54	19.684286	3.587677	11.54	21.42	16.417143	3.608373	13.67	21.34	...	14.440000	1.746749	15.21	26.38	21.832857	4.063753	17.04	21.84	19.155714	1.828705
1961-02-19	6.04	22.50	15.130000	5.064609	11.63	20.17	15.091429	3.575012	6.13	19.41	...	13.542857	2.531361	14.09	29.63	21.167143	5.910938	10.96	22.58	16.584286	4.685377
1961-02-26	7.79	25.80	15.221429	7.020716	7.08	21.50	13.625714	5.147348	6.08	22.42	...	12.730000	4.920064	9.59	23.21	16.304286	5.091162	6.67	23.87	14.322857	6.182283
1961-03-05	10.96	13.33	12.101429	0.997721	8.83	17.00	12.951429	2.851955	8.17	13.67	...	12.370000	1.593685	11.58	23.45	17.842857	4.332331	8.83	17.54	13.951667	3.021387
1961-03-12	4.88	14.79	9.376667	3.732263	8.08	16.96	11.578571	3.230167	7.54	16.38	...	10.458571	3.655113	10.21	22.71	16.701429	4.358759	5.54	22.54	14.420000	5.769890

10 rows × 48 columns

Conclusion

今天的pandas题更新，继续刷题，加油！