Vyprávíme příběh s daty¶

Seznámení s daty¶

Pandas 🐼¶

Nasťa a Filip Sedlákovi¶

Seznámení s daty - postup¶

Načíst data
- Případně přejmenovat sloupce
V jakém formátu jsou sloupce?
Kolik je řádků a sloupců?
Obsahují některé sloupce chybějící data?
Udělat popisnou statistiku dat

Co je pandas?¶

Knihovna pro analýzu dat v Pythonu
Je postavena na NumPy (knihovna pro práci s numerickými daty)
10 minutes to pandas
Srovnání Pandas s SQL

Jak naimportuji pandas?¶

In [1]:

# Importujeme pandas jako zkrátku
# Není to nutné, ale většina tutorialu to tak má
import pandas as pd

Jak načtu data?¶

Pandas má několik funkcí, tady je hezký přehled

Nejpoužívanější je funkce read_csv, která má více než 20 parametrů (nebojte se, jenom jeden parametr je povinný 🙂)

In [2]:

data = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/DAAG/primates.csv')

In [3]:

# Zobrazir první 2 řádky
data.head(n=2)

Out[3]:

	Unnamed: 0	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406

In [4]:

# Zapsát náhodné 3 řádky do CSV souboru
data1 = data.sample(n=3)
data1.to_csv('data1.csv')

In [5]:

# Zjistit datový typ tabulky
type(data)

Out[5]:

pandas.core.frame.DataFrame

In [6]:

# Zjistit datový typ sloupce
type(data.Bodywt)

Out[6]:

pandas.core.series.Series

In [7]:

primates = pd.read_csv('primates.csv')
primates.head()

Out[7]:

	Unnamed: 0	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406
2	Human	62.0	1320
3	Rhesus monkey	6.8	179
4	Chimp	52.2	440

In [8]:

# Zobrazíme index tabulky
primates.index

Out[8]:

RangeIndex(start=0, stop=5, step=1)

In [9]:

# Zobrazíme názvy sloupců tabulky
data.columns

Out[9]:

Index(['Unnamed: 0', 'Bodywt', 'Brainwt'], dtype='object')

Cvičení¶

Načtěte data tips.csv (jsou ve stejné složce) a použijte první sloupec jako index

Bonus: Načtete data z UNICEF pomocí read_excel funkce z pandas a přeskočte prvních 9 řádků

In [10]:

tips = pd.read_csv('tips.csv')
tips.head()

Out[10]:

	Unnamed: 0	total_bill	tip	sex	smoker	day	time	size
0	1	16.99	1.01	Female	No	Sun	Dinner	2
1	2	10.34	1.66	Male	No	Sun	Dinner	3
2	3	21.01	3.50	Male	No	Sun	Dinner	3
3	4	23.68	3.31	Male	No	Sun	Dinner	2
4	5	24.59	3.61	Female	No	Sun	Dinner	4

In [11]:

tips = pd.read_csv('tips.csv', index_col=0)
tips.head()

Out[11]:

	total_bill	tip	sex	smoker	day	time	size
1	16.99	1.01	Female	No	Sun	Dinner	2
2	10.34	1.66	Male	No	Sun	Dinner	3
3	21.01	3.50	Male	No	Sun	Dinner	3
4	23.68	3.31	Male	No	Sun	Dinner	2
5	24.59	3.61	Female	No	Sun	Dinner	4

In [12]:

# Bonus načtení Excelu
unicef = pd.read_excel('Out-of-School-Rate-Final-for-website.xlsx', 
                       skiprows=9
)
unicef.head()

Out[12]:

	ISO3 Code	Countries	Total	Unnamed: 3	Male	Unnamed: 5	Female	Unnamed: 7	Urban	Unnamed: 9	...	Poorer	Unnamed: 15	Middle	Unnamed: 17	Fourth	Unnamed: 19	Richest	Unnamed: 21	Source	Unnamed: 23
0	AFG	Afghanistan	36.3	NaN	26.9	NaN	46.8	NaN	19.7	NaN	...	46.2	NaN	44.4	NaN	31.8	NaN	16.7	NaN	DHS 2015	NaN
1	ALB	Albania	7.60232	x	7.76783	x	7.41396	x	6.66002	x	...	6.45801	x	8.8039	x	6.7463	x	6.39511	x	DHS 2008-09	NaN
2	DZA	Algeria	2.19084	NaN	2.1955	NaN	2.18592	NaN	1.94815	NaN	...	2.57707	NaN	1.7089	NaN	1.65181	NaN	1.42334	NaN	MICS 2012-13	NaN
3	AND	Andorra	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	AGO	Angola	23.7	y	22.8	y	24.6	y	15.1	y	...	25.8	y	22.5	y	14.4	y	9.7	y	IBEP 2008-09	NaN

5 rows × 24 columns

Jak můžu odstranít sloupce bez názvu?¶

In [13]:

# Zobrazíme všechny sloupce
unicef.columns

Out[13]:

Index(['ISO3 Code', 'Countries', 'Total', 'Unnamed: 3', 'Male', 'Unnamed: 5',
       'Female', 'Unnamed: 7', 'Urban', 'Unnamed: 9', 'Rural', 'Unnamed: 11',
       'Poorest', 'Unnamed: 13', 'Poorer', 'Unnamed: 15', 'Middle',
       'Unnamed: 17', 'Fourth', 'Unnamed: 19', 'Richest', 'Unnamed: 21',
       'Source', 'Unnamed: 23'],
      dtype='object')

Malá odbočka - list comprehensions¶

Dokumetace

Elegantní způsob jak transformovat seznamy.

In [14]:

# Zahřívací příklad: 
# nový seznam obsahuje všechny prvky původního seznamu
my_list = [1,2,3,4,5,6]
[item for item in my_list]

Out[14]:

[1, 2, 3, 4, 5, 6]

In [15]:

# Nový seznam obsahuje všechny prvky původního, 
# ale každý prvk je vynásoben 100
[item*100 for item in my_list]

Out[15]:

[100, 200, 300, 400, 500, 600]

In [16]:

# Nový seznam obsahuje jenom ty prvky seznamu, které jsou sudé
[item for item in my_list if item%2 == 0]

Out[16]:

[2, 4, 6]

In [17]:

# Obdobným způsobem vytvoříme seznam názvu sloupců, 
# které obsahují 'Unnamed' (částečná shoda)
# To jsou sloupce, které chceme vyhodit
col_to_drop = [
    col_name for col_name in unicef.columns if 'Unnamed' in col_name
]
col_to_drop

Out[17]:

['Unnamed: 3',
 'Unnamed: 5',
 'Unnamed: 7',
 'Unnamed: 9',
 'Unnamed: 11',
 'Unnamed: 13',
 'Unnamed: 15',
 'Unnamed: 17',
 'Unnamed: 19',
 'Unnamed: 21',
 'Unnamed: 23']

In [18]:

# Odstraníme z tabulky seznam sloupců, 
# které jsme vytvořili v předešlém kroku
unicef.drop(col_to_drop, axis='columns', inplace=True)
unicef.head()

Out[18]:

	ISO3 Code	Countries	Total	Male	Female	Urban	Rural	Poorest	Poorer	Middle	Fourth	Richest	Source
0	AFG	Afghanistan	36.3	26.9	46.8	19.7	41.4	40.6	46.2	44.4	31.8	16.7	DHS 2015
1	ALB	Albania	7.60232	7.76783	7.41396	6.66002	8.25574	8.8999	6.45801	8.8039	6.7463	6.39511	DHS 2008-09
2	DZA	Algeria	2.19084	2.1955	2.18592	1.94815	2.61943	3.4101	2.57707	1.7089	1.65181	1.42334	MICS 2012-13
3	AND	Andorra	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	AGO	Angola	23.7	22.8	24.6	15.1	33.2	37.2	25.8	22.5	14.4	9.7	IBEP 2008-09

Jak přejmenuji sloupce?¶

In [19]:

primates = pd.read_csv('primates.csv')
# Zobrazíme názvy sloupců
primates.columns

Out[19]:

Index(['Unnamed: 0', 'Bodywt', 'Brainwt'], dtype='object')

In [20]:

# inplace=True znamená, že se pozmění současná tabulka
primates.rename(
    columns={'Unnamed: 0': 'Species'}, 
    inplace=True
)
primates.head()

Out[20]:

	Species	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406
2	Human	62.0	1320
3	Rhesus monkey	6.8	179
4	Chimp	52.2	440

Cvičení¶

Načtěte data titanic3.xls pomocí read_excel funkce z pandas
Přejmenujte sloupec pclass na passenger_class a home.dest na home_destination

In [21]:

titanic = pd.read_excel('titanic3.xls')
titanic.head()

Out[21]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.0000	1	2	113781	151.5500	C22 C26	S	NaN	135.0	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON

In [22]:

titanic.columns

Out[22]:

Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',
       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],
      dtype='object')

In [23]:

titanic.rename(
    columns={
        'pclass': 'passenger_class', 
        'home.dest':'home_destination'
    },
    inplace=True
)
titanic.head()

Out[23]:

	passenger_class	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home_destination
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.0000	1	2	113781	151.5500	C22 C26	S	NaN	135.0	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON

Jak zjistím, v jakém formátu jsou sloupce?¶

In [24]:

primates.dtypes

Out[24]:

Species     object
Bodywt     float64
Brainwt      int64
dtype: object

In [25]:

titanic.dtypes

Out[25]:

passenger_class       int64
survived              int64
name                 object
sex                  object
age                 float64
sibsp                 int64
parch                 int64
ticket               object
fare                float64
cabin                object
embarked             object
boat                 object
body                float64
home_destination     object
dtype: object

Jak zjistím, kolik má tabulká řádů a sloupců?¶

In [26]:

# Počet řádků a počet sloupců
primates.shape

Out[26]:

(5, 3)

In [27]:

# Počet řádků
primates.shape[0]

Out[27]:

In [28]:

# Počet sloupců
primates.shape[1]

Out[28]:

In [29]:

# Počet řádků
len(primates)

Out[29]:

Cvičení¶

Načtěte data titanic3.xls pomocí read_excel funkce z pandas
Zjistěte počet řádků (správná odpověď je 1309)
Zjistěte počet sloupců (správná odpověď je 14)

In [30]:

titanic.shape[0]

Out[30]:

In [31]:

titanic.shape[1]

Out[31]:

Jak zjistím, zda v tabulce jsou chybějící data?¶

In [32]:

# Načteme data z Excelu
titanic = pd.read_excel('titanic3.xls')
titanic.head()

Out[32]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.0000	1	2	113781	151.5500	C22 C26	S	NaN	135.0	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON

In [33]:

# Jaké sloupce má tabulka?
titanic.columns

Out[33]:

Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',
       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],
      dtype='object')

In [34]:

# Má tabulká chybějící data?
titanic.isnull().values.any()

Out[34]:

True

In [35]:

# Zjistíme, které sloupce mají chybějící data
titanic.isnull().any()

Out[35]:

pclass       False
survived     False
name         False
sex          False
age           True
sibsp        False
parch        False
ticket       False
fare          True
cabin         True
embarked      True
boat          True
body          True
home.dest     True
dtype: bool

In [36]:

# Zobrazíme počet chybějících hodnot v každém sloupci
titanic.isnull().sum()

Out[36]:

pclass          0
survived        0
name            0
sex             0
age           263
sibsp           0
parch           0
ticket          0
fare            1
cabin        1014
embarked        2
boat          823
body         1188
home.dest     564
dtype: int64

Cvičení¶

Načtěte data YouthRisk2009.csv
Zjistěte, které sloupce mají chybějící data (správná odpověď Sleep, Sleep7, SmokeLife, SmokeDaily, MarijuaEver)

Bonus: Zjistěte kolík řádků má chybějící data (správná odpověď je 72)

In [37]:

youth_risk = pd.read_csv('YouthRisk2009.csv', index_col=0)
youth_risk.head()

Out[37]:

	Sleep	Sleep7	SmokeLife	SmokeDaily	MarijuaEver	Age
1	NaN	NaN	No	NaN	0.0	16
2	8 hours	1.0	Yes	Yes	1.0	16
3	5 hours	0.0	Yes	Yes	1.0	17
4	5 hours	0.0	Yes	Yes	1.0	18
5	7 hours	1.0	Yes	No	1.0	17

In [38]:

# Nastavíme maximální počet řádků, které se zobrazí při náhledu
pd.options.display.max_rows = 100

In [39]:

youth_risk.isnull().any(axis=1)

Out[39]:

1       True
2      False
3      False
4      False
5      False
6      False
7      False
8      False
9      False
10     False
11     False
12     False
13     False
14     False
15     False
16     False
17     False
18     False
19     False
20     False
21     False
22     False
23     False
24     False
25     False
26     False
27     False
28     False
29     False
30     False
31     False
32      True
33     False
34     False
35     False
36      True
37     False
38     False
39     False
40     False
41     False
42     False
43     False
44     False
45     False
46     False
47     False
48      True
49     False
50     False
       ...  
451    False
452    False
453    False
454    False
455    False
456    False
457    False
458    False
459    False
460    False
461    False
462     True
463    False
464    False
465    False
466    False
467    False
468    False
469    False
470    False
471    False
472    False
473     True
474    False
475    False
476    False
477    False
478    False
479    False
480    False
481    False
482    False
483    False
484    False
485    False
486    False
487    False
488    False
489    False
490    False
491    False
492    False
493    False
494    False
495    False
496    False
497    False
498    False
499    False
500    False
Length: 500, dtype: bool

In [40]:

# Počet řádků s chybějícími hodnotami
youth_risk.isnull().any(axis=1).sum()

Out[40]:

Jak vyberu řádky a sloupce?¶

Pandas má 2 způsoby:

iloc vybírá podle pozic (indexů)
- stejně jako když vybírám ze seznamu
- bez koncového indexu
loc vybírá podle jmen
- včetně koncového indexu

In [41]:

# Načteme data
primates = pd.read_csv('primates.csv')
primates.rename(index=str, columns={'Unnamed: 0': 'Species'}, inplace=True)
primates.head()

Out[41]:

	Species	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406
2	Human	62.0	1320
3	Rhesus monkey	6.8	179
4	Chimp	52.2	440

In [42]:

# Vybereme 0. a 1. řádky
primates.iloc[0:2]

Out[42]:

	Species	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406

In [43]:

# Vybereme 0. a 1. řádky a 1. a 2. sloupce
primates.iloc[0:2, [0,2]]

Out[43]:

	Species	Brainwt
0	Potar monkey	115
1	Gorilla	406

In [44]:

# Vybíráme podle jména
# POZOR: číselný index musí být převeden řetězec
primates.loc[:'2', ['Species', 'Bodywt']]

Out[44]:

	Species	Bodywt
0	Potar monkey	10.0
1	Gorilla	207.0
2	Human	62.0

In [45]:

# Vybereme sloupec
primates.Bodywt

Out[45]:

0     10.0
1    207.0
2     62.0
3      6.8
4     52.2
Name: Bodywt, dtype: float64

In [46]:

# Vybereme sloupec - druhy způsob
primates['Bodywt']

Out[46]:

0     10.0
1    207.0
2     62.0
3      6.8
4     52.2
Name: Bodywt, dtype: float64

In [47]:

# Lze specifikovat, který sloupec je index
# Index se v tomto případě skládá z řetězců
primates2 = pd.read_csv(
    'primates.csv', 
    index_col=0
)
primates2.head()

Out[47]:

	Bodywt	Brainwt
Potar monkey	10.0	115
Gorilla	207.0	406
Human	62.0	1320
Rhesus monkey	6.8	179
Chimp	52.2	440

In [48]:

primates2.loc['Potar monkey']

Out[48]:

Bodywt      10.0
Brainwt    115.0
Name: Potar monkey, dtype: float64

Cvičení¶

Načtěte data titanic
Vyberte prvních 20 řádků
Vyberte prvních 20 řádků a sloupec se jménem (name)

Bonus: Vyberte řádky 10 až 550 a následující sloupce: jméno (name), cena jízdenky (fare), zda osoba přežila (survived)

In [49]:

titanic.iloc[:20]

Out[49]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.0000	1	2	113781	151.5500	C22 C26	S	NaN	135.0	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
5	1	1	Anderson, Mr. Harry	male	48.0000	0	0	19952	26.5500	E12	S	3	NaN	New York, NY
6	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0000	1	0	13502	77.9583	D7	S	10	NaN	Hudson, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39.0000	0	0	112050	0.0000	A36	S	NaN	NaN	Belfast, NI
8	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53.0000	2	0	11769	51.4792	C101	S	D	NaN	Bayside, Queens, NY
9	1	0	Artagaveytia, Mr. Ramon	male	71.0000	0	0	PC 17609	49.5042	NaN	C	NaN	22.0	Montevideo, Uruguay
10	1	0	Astor, Col. John Jacob	male	47.0000	1	0	PC 17757	227.5250	C62 C64	C	NaN	124.0	New York, NY
11	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0000	1	0	PC 17757	227.5250	C62 C64	C	4	NaN	New York, NY
12	1	1	Aubart, Mme. Leontine Pauline	female	24.0000	0	0	PC 17477	69.3000	B35	C	9	NaN	Paris, France
13	1	1	Barber, Miss. Ellen "Nellie"	female	26.0000	0	0	19877	78.8500	NaN	S	6	NaN	NaN
14	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0000	0	0	27042	30.0000	A23	S	B	NaN	Hessle, Yorks
15	1	0	Baumann, Mr. John D	male	NaN	0	0	PC 17318	25.9250	NaN	S	NaN	NaN	New York, NY
16	1	0	Baxter, Mr. Quigg Edmond	male	24.0000	0	1	PC 17558	247.5208	B58 B60	C	NaN	NaN	Montreal, PQ
17	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0000	0	1	PC 17558	247.5208	B58 B60	C	6	NaN	Montreal, PQ
18	1	1	Bazzani, Miss. Albina	female	32.0000	0	0	11813	76.2917	D15	C	8	NaN	NaN
19	1	0	Beattie, Mr. Thomson	male	36.0000	0	0	13050	75.2417	C6	C	A	NaN	Winnipeg, MN

In [50]:

titanic.iloc[:20, 2]

Out[50]:

0                         Allen, Miss. Elisabeth Walton
1                        Allison, Master. Hudson Trevor
2                          Allison, Miss. Helen Loraine
3                  Allison, Mr. Hudson Joshua Creighton
4       Allison, Mrs. Hudson J C (Bessie Waldo Daniels)
5                                   Anderson, Mr. Harry
6                     Andrews, Miss. Kornelia Theodosia
7                                Andrews, Mr. Thomas Jr
8         Appleton, Mrs. Edward Dale (Charlotte Lamson)
9                               Artagaveytia, Mr. Ramon
10                               Astor, Col. John Jacob
11    Astor, Mrs. John Jacob (Madeleine Talmadge Force)
12                        Aubart, Mme. Leontine Pauline
13                         Barber, Miss. Ellen "Nellie"
14                 Barkworth, Mr. Algernon Henry Wilson
15                                  Baumann, Mr. John D
16                             Baxter, Mr. Quigg Edmond
17      Baxter, Mrs. James (Helene DeLaudeniere Chaput)
18                                Bazzani, Miss. Albina
19                                 Beattie, Mr. Thomson
Name: name, dtype: object

In [51]:

titanic.iloc[10:551, [1,2,8]]

Out[51]:

	survived	name	fare
10	0	Astor, Col. John Jacob	227.5250
11	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	227.5250
12	1	Aubart, Mme. Leontine Pauline	69.3000
13	1	Barber, Miss. Ellen "Nellie"	78.8500
14	1	Barkworth, Mr. Algernon Henry Wilson	30.0000
15	0	Baumann, Mr. John D	25.9250
16	0	Baxter, Mr. Quigg Edmond	247.5208
17	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	247.5208
18	1	Bazzani, Miss. Albina	76.2917
19	0	Beattie, Mr. Thomson	75.2417
20	1	Beckwith, Mr. Richard Leonard	52.5542
21	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	52.5542
22	1	Behr, Mr. Karl Howell	30.0000
23	1	Bidois, Miss. Rosalie	227.5250
24	1	Bird, Miss. Ellen	221.7792
25	0	Birnbaum, Mr. Jakob	26.0000
26	1	Bishop, Mr. Dickinson H	91.0792
27	1	Bishop, Mrs. Dickinson H (Helen Walton)	91.0792
28	1	Bissette, Miss. Amelia	135.6333
29	1	Bjornstrom-Steffansson, Mr. Mauritz Hakan	26.5500
30	0	Blackwell, Mr. Stephen Weart	35.5000
31	1	Blank, Mr. Henry	31.0000
32	1	Bonnell, Miss. Caroline	164.8667
33	1	Bonnell, Miss. Elizabeth	26.5500
34	0	Borebank, Mr. John James	26.5500
35	1	Bowen, Miss. Grace Scott	262.3750
36	1	Bowerman, Miss. Elsie Edith	55.0000
37	1	Bradley, Mr. George ("George Arthur Brayton")	26.5500
38	0	Brady, Mr. John Bertram	30.5000
39	0	Brandeis, Mr. Emil	50.4958
40	0	Brewe, Dr. Arthur Jackson	39.6000
41	1	Brown, Mrs. James Joseph (Margaret Tobin)	27.7208
42	1	Brown, Mrs. John Murray (Caroline Lane Lamson)	51.4792
43	1	Bucknell, Mrs. William Robert (Emma Eliza Ward)	76.2917
44	1	Burns, Miss. Elizabeth Margaret	134.5000
45	0	Butt, Major. Archibald Willingham	26.5500
46	0	Cairns, Mr. Alexander	31.0000
47	1	Calderhead, Mr. Edward Pennington	26.2875
48	1	Candee, Mrs. Edward (Helen Churchill Hungerford)	27.4458
49	1	Cardeza, Mr. Thomas Drake Martinez	512.3292
50	1	Cardeza, Mrs. James Warburton Martinez (Charlo...	512.3292
51	0	Carlsson, Mr. Frans Olof	5.0000
52	0	Carrau, Mr. Francisco M	47.1000
53	0	Carrau, Mr. Jose Pedro	47.1000
54	1	Carter, Master. William Thornton II	120.0000
55	1	Carter, Miss. Lucile Polk	120.0000
56	1	Carter, Mr. William Ernest	120.0000
57	1	Carter, Mrs. William Ernest (Lucile Polk)	120.0000
58	0	Case, Mr. Howard Brown	26.0000
59	1	Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...	27.7208
...	...	...	...
501	1	Mellinger, Miss. Madeleine Violet	19.5000
502	1	Mellinger, Mrs. (Elizabeth Anne Maidment)	19.5000
503	1	Mellors, Mr. William John	10.5000
504	0	Meyer, Mr. August	13.0000
505	0	Milling, Mr. Jacob Christian	13.0000
506	0	Mitchell, Mr. Henry Michael	10.5000
507	0	Montvila, Rev. Juozas	13.0000
508	0	Moraweck, Dr. Ernest	14.0000
509	0	Morley, Mr. Henry Samuel ("Mr Henry Marshall")	26.0000
510	0	Mudd, Mr. Thomas Charles	10.5000
511	0	Myles, Mr. Thomas Francis	9.6875
512	0	Nasser, Mr. Nicholas	30.0708
513	1	Nasser, Mrs. Nicholas (Adele Achem)	30.0708
514	1	Navratil, Master. Edmond Roger	26.0000
515	1	Navratil, Master. Michel M	26.0000
516	0	Navratil, Mr. Michel ("Louis M Hoffman")	26.0000
517	0	Nesson, Mr. Israel	13.0000
518	0	Nicholls, Mr. Joseph Charles	36.7500
519	0	Norman, Mr. Robert Douglas	13.5000
520	1	Nourney, Mr. Alfred ("Baron von Drachstedt")	13.8625
521	1	Nye, Mrs. (Elizabeth Ramell)	10.5000
522	0	Otter, Mr. Richard	13.0000
523	1	Oxenham, Mr. Percy Thomas	10.5000
524	1	Padro y Manent, Mr. Julian	13.8625
525	0	Pain, Dr. Alfred	10.5000
526	1	Pallas y Castello, Mr. Emilio	13.8583
527	0	Parker, Mr. Clifford Richard	10.5000
528	0	Parkes, Mr. Francis "Frank"	0.0000
529	1	Parrish, Mrs. (Lutie Davis)	26.0000
530	0	Pengelly, Mr. Frederick William	10.5000
531	0	Pernot, Mr. Rene	15.0500
532	0	Peruschitz, Rev. Joseph Maria	13.0000
533	1	Phillips, Miss. Alice Frances Louisa	21.0000
534	1	Phillips, Miss. Kate Florence ("Mrs Kate Louis...	26.0000
535	0	Phillips, Mr. Escott Robert	21.0000
536	1	Pinsky, Mrs. (Rosa)	13.0000
537	0	Ponesell, Mr. Martin	13.0000
538	1	Portaluppi, Mr. Emilio Ilario Giuseppe	12.7375
539	0	Pulbaum, Mr. Franz	15.0333
540	1	Quick, Miss. Phyllis May	26.0000
541	1	Quick, Miss. Winifred Vera	26.0000
542	1	Quick, Mrs. Frederick Charles (Jane Richards)	26.0000
543	0	Reeves, Mr. David	10.5000
544	0	Renouf, Mr. Peter Henry	21.0000
545	1	Renouf, Mrs. Peter Henry (Lillian Jefferys)	21.0000
546	1	Reynaldo, Ms. Encarnacion	13.0000
547	0	Richard, Mr. Emile	15.0458
548	1	Richards, Master. George Sibley	18.7500
549	1	Richards, Master. William Rowe	18.7500
550	1	Richards, Mrs. Sidney (Emily Hocking)	18.7500

541 rows × 3 columns

In [52]:

titanic.loc[10:550, ['name', 'fare', 'survived']]

Out[52]:

	name	fare	survived
10	Astor, Col. John Jacob	227.5250	0
11	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	227.5250	1
12	Aubart, Mme. Leontine Pauline	69.3000	1
13	Barber, Miss. Ellen "Nellie"	78.8500	1
14	Barkworth, Mr. Algernon Henry Wilson	30.0000	1
15	Baumann, Mr. John D	25.9250	0
16	Baxter, Mr. Quigg Edmond	247.5208	0
17	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	247.5208	1
18	Bazzani, Miss. Albina	76.2917	1
19	Beattie, Mr. Thomson	75.2417	0
20	Beckwith, Mr. Richard Leonard	52.5542	1
21	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	52.5542	1
22	Behr, Mr. Karl Howell	30.0000	1
23	Bidois, Miss. Rosalie	227.5250	1
24	Bird, Miss. Ellen	221.7792	1
25	Birnbaum, Mr. Jakob	26.0000	0
26	Bishop, Mr. Dickinson H	91.0792	1
27	Bishop, Mrs. Dickinson H (Helen Walton)	91.0792	1
28	Bissette, Miss. Amelia	135.6333	1
29	Bjornstrom-Steffansson, Mr. Mauritz Hakan	26.5500	1
30	Blackwell, Mr. Stephen Weart	35.5000	0
31	Blank, Mr. Henry	31.0000	1
32	Bonnell, Miss. Caroline	164.8667	1
33	Bonnell, Miss. Elizabeth	26.5500	1
34	Borebank, Mr. John James	26.5500	0
35	Bowen, Miss. Grace Scott	262.3750	1
36	Bowerman, Miss. Elsie Edith	55.0000	1
37	Bradley, Mr. George ("George Arthur Brayton")	26.5500	1
38	Brady, Mr. John Bertram	30.5000	0
39	Brandeis, Mr. Emil	50.4958	0
40	Brewe, Dr. Arthur Jackson	39.6000	0
41	Brown, Mrs. James Joseph (Margaret Tobin)	27.7208	1
42	Brown, Mrs. John Murray (Caroline Lane Lamson)	51.4792	1
43	Bucknell, Mrs. William Robert (Emma Eliza Ward)	76.2917	1
44	Burns, Miss. Elizabeth Margaret	134.5000	1
45	Butt, Major. Archibald Willingham	26.5500	0
46	Cairns, Mr. Alexander	31.0000	0
47	Calderhead, Mr. Edward Pennington	26.2875	1
48	Candee, Mrs. Edward (Helen Churchill Hungerford)	27.4458	1
49	Cardeza, Mr. Thomas Drake Martinez	512.3292	1
50	Cardeza, Mrs. James Warburton Martinez (Charlo...	512.3292	1
51	Carlsson, Mr. Frans Olof	5.0000	0
52	Carrau, Mr. Francisco M	47.1000	0
53	Carrau, Mr. Jose Pedro	47.1000	0
54	Carter, Master. William Thornton II	120.0000	1
55	Carter, Miss. Lucile Polk	120.0000	1
56	Carter, Mr. William Ernest	120.0000	1
57	Carter, Mrs. William Ernest (Lucile Polk)	120.0000	1
58	Case, Mr. Howard Brown	26.0000	0
59	Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...	27.7208	1
...	...	...	...
501	Mellinger, Miss. Madeleine Violet	19.5000	1
502	Mellinger, Mrs. (Elizabeth Anne Maidment)	19.5000	1
503	Mellors, Mr. William John	10.5000	1
504	Meyer, Mr. August	13.0000	0
505	Milling, Mr. Jacob Christian	13.0000	0
506	Mitchell, Mr. Henry Michael	10.5000	0
507	Montvila, Rev. Juozas	13.0000	0
508	Moraweck, Dr. Ernest	14.0000	0
509	Morley, Mr. Henry Samuel ("Mr Henry Marshall")	26.0000	0
510	Mudd, Mr. Thomas Charles	10.5000	0
511	Myles, Mr. Thomas Francis	9.6875	0
512	Nasser, Mr. Nicholas	30.0708	0
513	Nasser, Mrs. Nicholas (Adele Achem)	30.0708	1
514	Navratil, Master. Edmond Roger	26.0000	1
515	Navratil, Master. Michel M	26.0000	1
516	Navratil, Mr. Michel ("Louis M Hoffman")	26.0000	0
517	Nesson, Mr. Israel	13.0000	0
518	Nicholls, Mr. Joseph Charles	36.7500	0
519	Norman, Mr. Robert Douglas	13.5000	0
520	Nourney, Mr. Alfred ("Baron von Drachstedt")	13.8625	1
521	Nye, Mrs. (Elizabeth Ramell)	10.5000	1
522	Otter, Mr. Richard	13.0000	0
523	Oxenham, Mr. Percy Thomas	10.5000	1
524	Padro y Manent, Mr. Julian	13.8625	1
525	Pain, Dr. Alfred	10.5000	0
526	Pallas y Castello, Mr. Emilio	13.8583	1
527	Parker, Mr. Clifford Richard	10.5000	0
528	Parkes, Mr. Francis "Frank"	0.0000	0
529	Parrish, Mrs. (Lutie Davis)	26.0000	1
530	Pengelly, Mr. Frederick William	10.5000	0
531	Pernot, Mr. Rene	15.0500	0
532	Peruschitz, Rev. Joseph Maria	13.0000	0
533	Phillips, Miss. Alice Frances Louisa	21.0000	1
534	Phillips, Miss. Kate Florence ("Mrs Kate Louis...	26.0000	1
535	Phillips, Mr. Escott Robert	21.0000	0
536	Pinsky, Mrs. (Rosa)	13.0000	1
537	Ponesell, Mr. Martin	13.0000	0
538	Portaluppi, Mr. Emilio Ilario Giuseppe	12.7375	1
539	Pulbaum, Mr. Franz	15.0333	0
540	Quick, Miss. Phyllis May	26.0000	1
541	Quick, Miss. Winifred Vera	26.0000	1
542	Quick, Mrs. Frederick Charles (Jane Richards)	26.0000	1
543	Reeves, Mr. David	10.5000	0
544	Renouf, Mr. Peter Henry	21.0000	0
545	Renouf, Mrs. Peter Henry (Lillian Jefferys)	21.0000	1
546	Reynaldo, Ms. Encarnacion	13.0000	1
547	Richard, Mr. Emile	15.0458	0
548	Richards, Master. George Sibley	18.7500	1
549	Richards, Master. William Rowe	18.7500	1
550	Richards, Mrs. Sidney (Emily Hocking)	18.7500	1

541 rows × 3 columns

Počítání s celými sloupci¶

Pandas umí počítat s celým sloupcem najednou

Primáti:

Jaká je hmotnost mozku primátů v kilogramech?
Jaký je poměr mezi hmotností mozku a těla?

In [53]:

primates

Out[53]:

	Species	Bodywt	Brainwt
0	Potar monkey	10.0	115
1	Gorilla	207.0	406
2	Human	62.0	1320
3	Rhesus monkey	6.8	179
4	Chimp	52.2	440

In [54]:

brainwt_kg = primates.Brainwt / 1000
brainwt_kg

Out[54]:

0    0.115
1    0.406
2    1.320
3    0.179
4    0.440
Name: Brainwt, dtype: float64

In [55]:

brainwt_kg / primates.Bodywt

Out[55]:

0    0.011500
1    0.001961
2    0.021290
3    0.026324
4    0.008429
dtype: float64

In [56]:

# První způsob vytvoření nového sloupce
primates['ratio'] = brainwt_kg / primates.Bodywt
primates

Out[56]:

	Species	Bodywt	Brainwt	ratio
0	Potar monkey	10.0	115	0.011500
1	Gorilla	207.0	406	0.001961
2	Human	62.0	1320	0.021290
3	Rhesus monkey	6.8	179	0.026324
4	Chimp	52.2	440	0.008429

In [57]:

# První způsob vytvoření nového sloupce
primates.assign(ratio2=(brainwt_kg / primates.Bodywt), inplace=True)
primates

Out[57]:

	Species	Bodywt	Brainwt	ratio
0	Potar monkey	10.0	115	0.011500
1	Gorilla	207.0	406	0.001961
2	Human	62.0	1320	0.021290
3	Rhesus monkey	6.8	179	0.026324
4	Chimp	52.2	440	0.008429

In [58]:

# Nevektorové oprerace - iterujeme řádky tabulky
for row_index, row in primates.iterrows():
    print(row.Bodywt)

10.0
207.0
62.0
6.8
52.2

Jak vyberu řádky, které splňují určitou podmínku?¶

data[podmínka]

data[(podmínka1) & (podmínka2)]

In [59]:

primates2

Out[59]:

	Bodywt	Brainwt
Potar monkey	10.0	115
Gorilla	207.0	406
Human	62.0	1320
Rhesus monkey	6.8	179
Chimp	52.2	440

In [60]:

primates2.columns

Out[60]:

Index(['Bodywt', 'Brainwt'], dtype='object')

In [61]:

primates2.Bodywt

Out[61]:

Potar monkey      10.0
Gorilla          207.0
Human             62.0
Rhesus monkey      6.8
Chimp             52.2
Name: Bodywt, dtype: float64

In [62]:

primates2.Bodywt > 60

Out[62]:

Potar monkey     False
Gorilla           True
Human             True
Rhesus monkey    False
Chimp            False
Name: Bodywt, dtype: bool

In [63]:

#  ~ znamená negace
~(primates2.Brainwt < 500)

Out[63]:

Potar monkey     False
Gorilla          False
Human             True
Rhesus monkey    False
Chimp            False
Name: Brainwt, dtype: bool

In [64]:

# | znamená nebo
primates2[(primates2.Bodywt > 60) | (primates2.Brainwt >= 500)]

Out[64]:

	Bodywt	Brainwt
Gorilla	207.0	406
Human	62.0	1320

In [65]:

# Vyberéme řádky, kde tělesná hmota je více než 60
primates2[primates2.Bodywt > 60]

Out[65]:

	Bodywt	Brainwt
Gorilla	207.0	406
Human	62.0	1320

In [66]:

# Vyberéme řádky, kde je tělesná hmota více než 60 a
# hmota mozku je méně než 500
primates2[(primates2.Bodywt > 60) & (primates2.Brainwt < 500)]

Out[66]:

	Bodywt	Brainwt
Gorilla	207.0	406

Cvičení¶

Načtěte data titanic
Vyberte řádky cestujících ženského pohlaví a spočítejte je (správná odpověď je 466)
Vyberte řádky cestujících ženského pohlaví, které jsou starší 1 roku a spočítejte je (správná odpověď je 379)

Bonus: Pro vybrané řádky spočítejte průměrnou cenu jízdenky (fare) (správná odpověď je cca 51)

In [67]:

sum(titanic.sex == 'female')

Out[67]:

In [68]:

len(titanic[titanic.sex == 'female'])

Out[68]:

In [69]:

titanic[titanic.sex == 'female'].shape[0]

Out[69]:

In [70]:

sum((titanic.age > 1) & (titanic.sex == 'female'))

Out[70]:

In [71]:

titanic[(titanic.age > 1) & (titanic.sex == 'female')].fare.mean()

Out[71]:

51.017481530343005

Jak seskupím řádky?¶

Použijeme metodu groupby, která seskupí řádky dle jednoho nebo více sloupců.

Potom použijeme nějakou metodu nad seskupenými řádky (count, mean, sum).

In [72]:

# Načteme data z Excelu
data = pd.read_excel('titanic3.xls')
data.head()

Out[72]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30.0000	1	2	113781	151.5500	C22 C26	S	NaN	135.0	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON

In [73]:

# Groupby vrací iterátor
groups = titanic.groupby('sex')

In [74]:

# Unikátní hodnoty dle kterých je rozdělena tabulka
groups.groups.keys()

Out[74]:

dict_keys(['female', 'male'])

In [75]:

# Seskupíme řádky dle pohlaví a spočítáme průměrnou hodnotu všech číselných proměnných
data.groupby('sex').mean()

Out[75]:

	pclass	survived	age	sibsp	parch	fare	body
sex
female	2.154506	0.727468	28.687071	0.652361	0.633047	46.198097	166.62500
male	2.372479	0.190985	30.585233	0.413998	0.247924	26.154601	160.39823

In [76]:

# Totéž můžeme udělat pomocí filtrování
titanic[titanic.sex == 'female'].mean()

Out[76]:

pclass        2.154506
survived      0.727468
age          28.687071
sibsp         0.652361
parch         0.633047
fare         46.198097
body        166.625000
dtype: float64

In [77]:

titanic[titanic.sex == 'male'].mean()

Out[77]:

pclass        2.372479
survived      0.190985
age          30.585233
sibsp         0.413998
parch         0.247924
fare         26.154601
body        160.398230
dtype: float64

In [78]:

# Ve smyčce vytiskneme průměrné hodnoty pro cestující obou pohlaví
for sex in titanic.sex.unique():
    print(sex, titanic[titanic.sex == sex].mean())

female pclass        2.154506
survived      0.727468
age          28.687071
sibsp         0.652361
parch         0.633047
fare         46.198097
body        166.625000
dtype: float64
male pclass        2.372479
survived      0.190985
age          30.585233
sibsp         0.413998
parch         0.247924
fare         26.154601
body        160.398230
dtype: float64

In [79]:

# Seskupení dle více proměnných - pohlaví a místa nalodění
titanic.groupby(['sex', 'embarked', 'survived']).mean()

Out[79]:

			pclass	age	sibsp	parch	fare	body
sex	embarked	survived
female	C	0	2.636364	27.071429	0.454545	0.454545	17.460991	328.000000
	C	1	1.539216	31.543956	0.509804	0.519608	87.994648	NaN
	Q	0	3.000000	26.692308	0.086957	0.347826	9.311961	229.000000
	Q	1	2.837838	24.000000	0.270270	0.000000	14.564192	NaN
	S	0	2.806452	24.842105	1.333333	1.075269	26.117341	79.500000
	S	1	1.924242	29.104167	0.560606	0.651515	45.549622	NaN
male	C	0	2.119266	35.178082	0.302752	0.201835	42.555965	155.083333
	C	1	1.729167	29.912602	0.375000	0.416667	63.013896	NaN
	Q	0	2.875000	32.104167	0.500000	0.107143	12.561384	134.750000
	Q	1	3.000000	25.000000	0.285714	0.000000	9.974400	NaN
	S	0	2.460348	30.855829	0.444874	0.241779	20.722284	163.105882
	S	1	2.188679	25.712862	0.358491	0.339623	27.291978	NaN

Cvičení¶

Seskupte titanic dle třídy (pclass) a pohlaví (sex) zobrazte medián (median)

Bonus: zobrazte jenom sloupec věk (age)

In [80]:

titanic.groupby(['pclass', 'sex']).median()

Out[80]:

		survived	age	sibsp	parch	fare	body
pclass	sex
1	female	1	36.0	0	0	80.92915	NaN
1	male	0	42.0	0	0	49.50420	166.0
2	female	1	28.0	0	0	23.00000	52.0
2	male	0	29.5	0	0	13.00000	160.0
3	female	0	22.0	0	0	10.48960	206.0
3	male	0	25.0	0	0	7.89580	148.0

In [81]:

# Pokud seskupojeme podle 2 a více proměnných, výsledná tabulka 
# bude mít několikaúrovňový index
titanic.groupby(['pclass', 'sex']).median()['age'].index

Out[81]:

MultiIndex(levels=[[1, 2, 3], ['female', 'male']],
           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
           names=['pclass', 'sex'])

In [82]:

titanic.groupby(['pclass',]).median()['age'].index

Out[82]:

Int64Index([1, 2, 3], dtype='int64', name='pclass')

In [83]:

titanic.groupby(['pclass', 'sex']).median().age

Out[83]:

pclass  sex   
1       female    36.0
        male      42.0
2       female    28.0
        male      29.5
3       female    22.0
        male      25.0
Name: age, dtype: float64

Jak ukážu popisnou statistiku pro všechny sloupce?¶

In [84]:

# Bez include='all' jenom numerické sloupce
titanic.describe(include='all')

Out[84]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
count	1309.000000	1309.000000	1309	1309	1046.000000	1309.000000	1309.000000	1309	1308.000000	295	1307	486	121.000000	745
unique	NaN	NaN	1307	2	NaN	NaN	NaN	939	NaN	186	3	28	NaN	369
top	NaN	NaN	Connolly, Miss. Kate	male	NaN	NaN	NaN	CA. 2343	NaN	C23 C25 C27	S	13	NaN	New York, NY
freq	NaN	NaN	2	843	NaN	NaN	NaN	11	NaN	6	914	39	NaN	64
mean	2.294882	0.381971	NaN	NaN	29.881135	0.498854	0.385027	NaN	33.295479	NaN	NaN	NaN	160.809917	NaN
std	0.837836	0.486055	NaN	NaN	14.413500	1.041658	0.865560	NaN	51.758668	NaN	NaN	NaN	97.696922	NaN
min	1.000000	0.000000	NaN	NaN	0.166700	0.000000	0.000000	NaN	0.000000	NaN	NaN	NaN	1.000000	NaN
25%	2.000000	0.000000	NaN	NaN	21.000000	0.000000	0.000000	NaN	7.895800	NaN	NaN	NaN	72.000000	NaN
50%	3.000000	0.000000	NaN	NaN	28.000000	0.000000	0.000000	NaN	14.454200	NaN	NaN	NaN	155.000000	NaN
75%	3.000000	1.000000	NaN	NaN	39.000000	1.000000	0.000000	NaN	31.275000	NaN	NaN	NaN	256.000000	NaN
max	3.000000	1.000000	NaN	NaN	80.000000	8.000000	9.000000	NaN	512.329200	NaN	NaN	NaN	328.000000	NaN

In [85]:

# Bez include='all' jenom numerické sloupce
titanic.describe()

Out[85]:

	pclass	survived	age	sibsp	parch	fare	body
count	1309.000000	1309.000000	1046.000000	1309.000000	1309.000000	1308.000000	121.000000
mean	2.294882	0.381971	29.881135	0.498854	0.385027	33.295479	160.809917
std	0.837836	0.486055	14.413500	1.041658	0.865560	51.758668	97.696922
min	1.000000	0.000000	0.166700	0.000000	0.000000	0.000000	1.000000
25%	2.000000	0.000000	21.000000	0.000000	0.000000	7.895800	72.000000
50%	3.000000	0.000000	28.000000	0.000000	0.000000	14.454200	155.000000
75%	3.000000	1.000000	39.000000	1.000000	0.000000	31.275000	256.000000
max	3.000000	1.000000	80.000000	8.000000	9.000000	512.329200	328.000000

Cvičení¶

Použijte následující postup a seznamte se s těmito daty, popis dat

Načíst data
- Případně přejmenovat sloupce
V jakém formátu jsou sloupce?
Kolik je řádků a sloupců?
Obsahují některé sloupce chybějící data?
Udělat popisnou statistiku dat

Načíst data¶

In [86]:

education = pd.read_csv(
    'http://vincentarelbundock.github.io/Rdatasets/csv/robustbase/education.csv',
    index_col=0
)
education.head()

Out[86]:

	State	Region	X1	X2	X3	Y
1	ME	1	508	3944	325	235
2	NH	1	564	4578	323	231
3	VT	1	322	4011	328	270
4	MA	1	846	5233	305	261
5	RI	1	871	4780	303	300

Přejmenovat sloupce¶

In [87]:

education.rename(columns={
    'X1': 'residents', 'X2': 'income', 
    'X3': 'residents_young', 'Y': 'expenditure'
    }, 
    inplace=True
)
education.head()

Out[87]:

	State	Region	residents	income	residents_young	expenditure
1	ME	1	508	3944	325	235
2	NH	1	564	4578	323	231
3	VT	1	322	4011	328	270
4	MA	1	846	5233	305	261
5	RI	1	871	4780	303	300

V jakém formátu jsou sloupce?¶

In [88]:

education.dtypes

Out[88]:

State              object
Region              int64
residents           int64
income              int64
residents_young     int64
expenditure         int64
dtype: object

Kolik je řádků a sloupců?¶

In [89]:

education.shape

Out[89]:

(50, 6)

Obsahují některé sloupce chybějící data?¶

In [90]:

education.isnull().any()

Out[90]:

State              False
Region             False
residents          False
income             False
residents_young    False
expenditure        False
dtype: bool

Udělat popisnou statistiku dat¶

In [91]:

education.describe(include='all')

Out[91]:

	State	Region	residents	income	residents_young	expenditure
count	50	50.000000	50.000000	50.000000	50.000000	50.000000
unique	50	NaN	NaN	NaN	NaN	NaN
top	CO	NaN	NaN	NaN	NaN	NaN
freq	1	NaN	NaN	NaN	NaN	NaN
mean	NaN	2.660000	657.800000	4675.120000	325.740000	284.600000
std	NaN	1.061574	145.016396	644.506254	19.423119	61.340136
min	NaN	1.000000	322.000000	3448.000000	287.000000	208.000000
25%	NaN	2.000000	546.750000	4137.250000	310.750000	234.250000
50%	NaN	3.000000	662.500000	4706.000000	324.500000	269.500000
75%	NaN	3.750000	782.250000	5054.250000	333.000000	316.750000
max	NaN	4.000000	909.000000	5889.000000	386.000000	546.000000