58 read csv and excel

https://vlrtraining.com/courses/python-data-science-beginner-tutorial

In [5]:

import numpy as np
import pandas as pd
df = pd.read_csv('mycsv.csv')

In [11]:

df=pd.read_csv('mycsv.csv')

In [13]:

df.to_csv('mycsv2.csv',index=False)

In [20]:

df2=pd.read_excel('my1ex.xlsx',sheet_name='Sheet2')

In [14]:

pd.read_excel('Excel_Sample.xlsx',sheet_name='Sheet2')

Out[14]:

	a	b	c	d
0	0	1	2	3
1	4	5	6	7
2	666	9	10	11
3	12	13	14	15

In [21]:

df2.to_excel('ram1.xlsx',sheet_name='Sheet1')

In [14]:

data = {'A':['foo','foo','foo','bar','bar','bar'],
     'B':['one','one','two','two','one','one'],
       'C':['x','y','x','y','x','y'],
       'D':[1,3,2,5,4,1]}

df1 = pd.DataFrame(data)

In [15]:

df1

Out[15]:

	A	B	C	D
0	foo	one	x	1
1	foo	one	y	3
2	foo	two	x	2
3	bar	two	y	5
4	bar	one	x	4
5	bar	one	y	1

In [17]:

df1.to_csv('ram.csv',sep='@')

In [24]:

df = pd.read_html('https://en.wikipedia.org/wiki/List_of_mandals_in_Andhra_Pradesh')

In [28]:

conda install lxml
conda install html5lib
conda install BeautifulSoup4

In [30]:

df[1]

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-30-dc0ea82142cb> in <module>()
----> 1 df[1]

IndexError: list index out of range

In [31]:

df

Out[31]:

[                    0               1  \
 0            District  No. of Mandals   
 1           Anantapur              63   
 2         Chittoor[3]              66   
 3           Kadapa[4]              51   
 4    East Godavari[5]              64   
 5           Guntur[6]              57   
 6          Krishna[7]              53   
 7          Kurnool[9]              54   
 8         Nellore[10]              46   
 9        Prakasam[11]              56   
 10     Srikakulam[12]              38   
 11  Visakhapatnam[13]              46   
 12   Vizianagaram[15]              34   
 13  West Godavari[16]              48   
 14      Total Mandals             664   
 
                                                     2  
 0                                        Mandal Names  
 1   Agali Amadagur Amarapuram Anantapur Atmakur Ba...  
 2   B. Kothakota Baireddipalle Bangarupalem Buchin...  
 3   Atlur B.Kodur Badvel Brahmamgarimatham Chakray...  
 4   Amalapuram Addateegala Ainavilli Alamuru Allav...  
 5   Amaravati Amruthalur Achampet Bapatla Bhattipr...  
 6   A.Konduru Agiripalli Avanigadda Bantumilli Bap...  
 7   Adoni Allagadda Alur Aspari Atmakur Banaganapa...  
 8   Allur Ananthasagaram Anumasamudrampeta Atmakur...  
 9   Addanki Ardhaveedu Ballikuruva Bestavaripeta C...  
 10  Amadalavalasa Bhamini Burja Etcherla Gara Gang...  
 11  Anakapalli Anandapuram Ananthagiri Araku Valle...  
 12  Bhogapuram Badangi Bondapalli Balijipeta Bobbi...  
 13  Attili Akiveedu Achanta Buttayagudem Bhimavara...  
 14                                                NaN  ]

In [32]:

df4 = pd.read_html('https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)')

In [38]:

df4[2]

Out[38]:

	0	1	2	3	4	5	6
0	Rank	Country or area	UN continental region[2]	UN statistical region[2]	Population (1 July 2016)[3]	Population (1 July 2017)[3]	Change
1	—	World	—	—	7466964280	7550262101	7000111555135228260♠+1.1%
2	1	China[a]	Asia	Eastern Asia	1403500365	1409517397	6999428716097982630♠+0.4%
3	2	India	Asia	Southern Asia	1324171354	1339180127	7000113344643460699♠+1.1%
4	3	United States	Americas	Northern America	322179605	324459463	6999707635730076710♠+0.7%
5	4	Indonesia	Asia	South-eastern Asia	261115456	263991379	7000110139899186970♠+1.1%
6	5	Brazil	Americas	South America	207652865	209288278	6999787570641031120♠+0.8%
7	6	Pakistan	Asia	Southern Asia	193203476	197015955	7000197329731272540♠+2.0%
8	7	Nigeria	Africa	Western Africa	185989640	190886311	7000263276545940949♠+2.6%
9	8	Bangladesh	Asia	Southern Asia	162951560	164669751	7000105441825779390♠+1.1%
10	9	Russia	Europe	Eastern Europe	143964513	143989754	5000000000000000000♠0.0%
11	10	Mexico	Americas	Central America	127540423	129163276	7000127242246954130♠+1.3%
12	11	Japan	Asia	Eastern Asia	127748513	127484450	3000793294658545260♠−0.2%
13	12	Ethiopia	Africa	Eastern Africa	102403196	104957438	7000249429910371160♠+2.5%
14	13	Philippines	Asia	South-eastern Asia	103320222	104918090	7000154652009942450♠+1.5%
15	14	Egypt	Africa	Northern Africa	95688681	97553151	7000194847497166360♠+1.9%
16	15	Vietnam	Asia	South-eastern Asia	94569072	95540800	7000102753255313749♠+1.0%
17	16	Germany	Europe	Western Europe	81914672	82114224	6999243609594139630♠+0.2%
18	17	Democratic Republic of the Congo	Africa	Middle Africa	78736153	81339988	7000330703863573320♠+3.3%
19	18	Iran	Asia	Southern Asia	80277428	81162788	7000110287539356640♠+1.1%
20	19	Turkey	Asia	Western Asia	79512426	80745020	7000155019040671710♠+1.6%
21	20	Thailand	Asia	South-eastern Asia	68863514	69037513	6999252672264154290♠+0.3%
22	21	United Kingdom	Europe	Northern Europe	65788574	66181585	6999597384889357830♠+0.6%
23	22	France[b]	Europe	Western Europe	64720690	64979548	6999399961743300330♠+0.4%
24	23	Italy	Europe	Southern Europe	59429938	59359900	3000882150306130220♠−0.1%
25	24	Tanzania[c]	Africa	Eastern Africa	55572201	57310019	7000312713545392960♠+3.1%
26	25	South Africa	Africa	Southern Africa	56015473	56717156	7000125265924292030♠+1.3%
27	26	Myanmar	Asia	South-eastern Asia	52885223	53370609	6999917810254860800♠+0.9%
28	27	South Korea	Asia	Eastern Asia	50791919	50982212	6999374652117396870♠+0.4%
29	28	Colombia	Americas	South America	48653419	49065615	6999847208702845740♠+0.8%
...	...	...	...	...	...	...	...
205	204	Dominica	Americas	Caribbean	73543	73925	6999519424010442870♠+0.5%
206	205	Cayman Islands	Americas	Caribbean	60765	61559	7000130667324940339♠+1.3%
207	206	Bermuda	Americas	Northern America	61666	61349	3000485940388544740♠−0.5%
208	207	Greenland	Americas	Northern America	56412	56480	6999120541728710210♠+0.1%
209	208	American Samoa	Oceania	Polynesia	55599	55641	6998755409269950880♠+0.1%
210	209	Saint Kitts and Nevis	Americas	Caribbean	54821	55345	6999955838091242400♠+1.0%
211	210	Northern Mariana Islands	Oceania	Micronesia	55023	55144	6999219908038456640♠+0.2%
212	211	Marshall Islands	Oceania	Micronesia	53066	53127	6999114951192854180♠+0.1%
213	212	Faroe Islands	Europe	Northern Europe	49117	49290	6999352220208888990♠+0.4%
214	213	Sint Maarten	Americas	Caribbean	39537	40120	7000147456812605910♠+1.5%
215	214	Monaco	Europe	Western Europe	38499	38695	6999509104132574879♠+0.5%
216	215	Liechtenstein	Europe	Western Europe	37666	37922	6999679658047045080♠+0.7%
217	216	Turks and Caicos Islands	Americas	Caribbean	34900	35446	7000156446991404010♠+1.6%
218	217	Gibraltar	Europe	Southern Europe	34408	34571	6999473727040223190♠+0.5%
219	218	San Marino	Europe	Southern Europe	33203	33400	6999593319880733680♠+0.6%
220	219	British Virgin Islands	Americas	Caribbean	30661	31196	7000174488764228170♠+1.7%
221	220	Caribbean Netherlands[s]	Americas	Caribbean	25019	25398	7000151484871497660♠+1.5%
222	221	Palau	Oceania	Micronesia	21503	21729	7000105101613728320♠+1.1%
223	222	Cook Islands	Oceania	Polynesia	17379	17380	5000000000000000000♠0.0%
224	223	Anguilla	Americas	Caribbean	14764	14909	6999982118667027910♠+1.0%
225	224	Wallis and Futuna	Oceania	Polynesia	11899	11773	2999894108748634340♠−1.1%
226	225	Nauru	Oceania	Micronesia	11347	11359	6999105754825063900♠+0.1%
227	226	Tuvalu	Oceania	Polynesia	11097	11192	6999856087230783100♠+0.9%
228	227	Saint Pierre and Miquelon	Americas	Northern America	6305	6320	6999237906423473430♠+0.2%
229	228	Montserrat	Americas	Caribbean	5152	5177	6999485248447204970♠+0.5%
230	229	Saint Helena, Ascension and Tristan da Cunha	Africa	Western Africa	4035	4049	6999346964064436170♠+0.3%
231	230	Falkland Islands	Americas	South America	2910	2910	5000000000000000000♠0.0%
232	231	Niue	Oceania	Polynesia	1624	1618	3000630541871921189♠−0.4%
233	232	Tokelau	Oceania	Polynesia	1282	1300	7000140405616224650♠+1.4%
234	233	Vatican City[t]	Europe	Southern Europe	801	792	2999887640449438199♠−1.1%

235 rows × 7 columns

In [43]:

df4[0].to_excel('ram311.xlsx',sheet_name='Sheet1')