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。
We are going to use a subset of US Baby Names from Kaggle.
In the file it will be names from 2004 until 2014
代码如下:
import pandas as pd
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
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 |
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 |
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
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 |
代码如下:
len(names)
输出结果如下:
17632
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'
代码如下:
len(names[names.Count == names.Count.min()])
输出结果如下:
2578
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
代码如下:
names.Count.std()
输出结果如下:
11006.069467891111
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 |
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.
""" 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.
代码如下:
import pandas as pd import datetime
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 |
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 |
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 |
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
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
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
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 |
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 |
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
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 |
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 |
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 |
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
今天的pandas题更新,继续刷题,加油!