Dataframe-input,output(csv, exel ve html) dosyaları
In [1]:
import pandas as pd # her zamanki gibi öncelikle kullanacağımız pandas kütüphanesini import ediyoruz.
data.gov sitesinden herhangi bir csv dosyası buldum. “https://data.cdc.gov/api/views/bi63-dtpu/rows.csv” download linkini csv ye kadar koplayayıp aldım.
In [5]:
df=pd.read_csv("https://data.cdc.gov/api/views/bi63-dtpu/rows.csv")
# okuma süresi yüklediğimiz dosyanın boyutuna göre değişkenlik gösterir
In [6]:
df
Out[6]:
| Year | 113 Cause Name | Cause Name | State | Deaths | Age-adjusted Death Rate | |
|---|---|---|---|---|---|---|
| 0 | 2012 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 21 | 2.6 |
| 1 | 2016 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 30 | 3.7 |
| 2 | 2013 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 30 | 3.8 |
| 3 | 2000 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 23 | 3.8 |
| 4 | 2014 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 325 | 4.1 |
| 5 | 2009 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 29 | 4.4 |
| 6 | 2011 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | South Dakota | 49 | 4.5 |
| 7 | 2014 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 37 | 4.5 |
| 8 | 2015 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 39 | 4.5 |
| 9 | 2013 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 374 | 4.9 |
| 10 | 2015 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 34 | 4.9 |
| 11 | 2001 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Washington | 279 | 5.0 |
| 12 | 1999 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 30 | 5.1 |
| 13 | 2016 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 40 | 5.2 |
| 14 | 1999 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Washington | 278 | 5.2 |
| 15 | 2002 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 31 | 5.2 |
| 16 | 2006 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 30 | 5.2 |
| 17 | 2003 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Washington | 304 | 5.3 |
| 18 | 2011 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 43 | 5.4 |
| 19 | 2002 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Washington | 306 | 5.4 |
| 20 | 2000 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Washington | 293 | 5.4 |
| 21 | 2008 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Vermont | 39 | 5.4 |
| 22 | 2005 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 33 | 5.4 |
| 23 | 2015 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 458 | 5.5 |
| 24 | 2004 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 33 | 5.5 |
| 25 | 2012 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | South Dakota | 58 | 5.6 |
| 26 | 2011 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 395 | 5.6 |
| 27 | 2011 | Intentional self-harm (suicide) (*U03,X60-X84,… | Suicide | District of Columbia | 37 | 5.6 |
| 28 | 2016 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 487 | 5.7 |
| 29 | 2012 | Nephritis, nephrotic syndrome and nephrosis (N… | Kidney disease | Arizona | 414 | 5.7 |
| … | … | … | … | … | … | … |
| 10266 | 2002 | All Causes | All causes | West Virginia | 21016 | 999.0 |
| 10267 | 2006 | All Causes | All causes | Mississippi | 28564 | 999.1 |
| 10268 | 2001 | All Causes | All causes | West Virginia | 20967 | 1000.9 |
| 10269 | 2005 | All Causes | All causes | Alabama | 47090 | 1001.3 |
| 10270 | 2001 | All Causes | All causes | Alabama | 45316 | 1002.1 |
| 10271 | 2003 | All Causes | All causes | West Virginia | 21306 | 1003.1 |
| 10272 | 1999 | All Causes | All causes | Kentucky | 39321 | 1004.1 |
| 10273 | 2000 | All Causes | All causes | Alabama | 45062 | 1004.8 |
| 10274 | 2000 | All Causes | All causes | Louisiana | 41138 | 1006.3 |
| 10275 | 2002 | All Causes | All causes | Louisiana | 41984 | 1008.5 |
| 10276 | 1999 | All Causes | All causes | Alabama | 44806 | 1009.3 |
| 10277 | 2002 | All Causes | All causes | Kentucky | 40697 | 1009.7 |
| 10278 | 2000 | All Causes | All causes | West Virginia | 21114 | 1011.1 |
| 10279 | 1999 | All Causes | All causes | West Virginia | 21049 | 1012.3 |
| 10280 | 2001 | All Causes | All causes | Louisiana | 41757 | 1013.1 |
| 10281 | 2002 | All Causes | All causes | Alabama | 46069 | 1013.4 |
| 10282 | 2003 | All Causes | All causes | Louisiana | 42719 | 1013.7 |
| 10283 | 2003 | All Causes | All causes | Alabama | 46716 | 1020.2 |
| 10284 | 1999 | All Causes | All causes | Louisiana | 41238 | 1021.6 |
| 10285 | 2005 | All Causes | All causes | Louisiana | 44355 | 1023.7 |
| 10286 | 2005 | All Causes | All causes | Mississippi | 29196 | 1028.7 |
| 10287 | 2003 | All Causes | All causes | Mississippi | 28489 | 1031.6 |
| 10288 | 2002 | All Causes | All causes | District of Columbia | 5851 | 1034.1 |
| 10289 | 2001 | All Causes | All causes | Mississippi | 28259 | 1034.3 |
| 10290 | 1999 | All Causes | All causes | Mississippi | 28185 | 1043.4 |
| 10291 | 2001 | All Causes | All causes | District of Columbia | 5951 | 1049.9 |
| 10292 | 2002 | All Causes | All causes | Mississippi | 28853 | 1051.6 |
| 10293 | 2000 | All Causes | All causes | Mississippi | 28654 | 1051.9 |
| 10294 | 2000 | All Causes | All causes | District of Columbia | 6001 | 1061.2 |
| 10295 | 1999 | All Causes | All causes | District of Columbia | 6076 | 1087.3 |
10296 rows × 6 columns
In [7]:
df["State"].tolist() # seçtiğimiz bir sütunu python listesine çevirme işlemi
Out[7]:
['Vermont', 'Vermont', 'Vermont', 'District of Columbia', 'Arizona', 'District of Columbia', 'South Dakota', 'Vermont', 'Vermont', 'Arizona', 'District of Columbia', 'Washington', 'District of Columbia', 'District of Columbia', 'Washington', 'District of Columbia', 'District of Columbia', 'Washington', 'Vermont', 'Washington', 'Washington', 'Vermont', 'District of Columbia', 'Arizona', 'District of Columbia', 'South Dakota', 'Arizona', 'District of Columbia', 'Arizona', 'Arizona', 'District of Columbia', 'District of Columbia', 'South Dakota', 'Iowa', 'Washington', 'Washington', 'South Dakota', 'New York', 'New York', 'Iowa', 'New York', 'New York', 'District of Columbia', 'Massachusetts', 'Washington', 'New Jersey', 'District of Columbia', 'Washington', 'California', 'North Dakota', 'New Jersey', 'North Dakota', 'New York', 'New Jersey', 'New York', 'Rhode Island', 'North Dakota', 'Iowa', 'Iowa', 'South Dakota', 'Massachusetts', 'Iowa', 'South Dakota', 'Massachusetts', 'Massachusetts', 'New Jersey', 'New York', 'District of Columbia', 'North Dakota', 'North Dakota', 'Washington', 'Washington', 'Massachusetts', 'New Jersey', 'New Jersey', 'Washington', 'Vermont', 'Massachusetts', 'New York', 'New Jersey', 'Massachusetts', 'New Jersey', 'Washington', 'Wyoming', 'Washington', 'California', 'Vermont', 'Oregon', 'Alaska', 'New Jersey', 'New Jersey', 'New Jersey', 'Iowa', 'Oregon', 'North Dakota', 'Oregon', 'District of Columbia', 'California', 'Vermont', 'Idaho', 'New York', 'New York', 'Vermont', 'Iowa', 'Alaska', 'Washington', 'New York', 'New York', 'Rhode Island', 'District of Columbia', 'California', 'Iowa', 'Oregon', 'New Jersey', 'Alaska', 'California', 'California', 'Massachusetts', 'Oregon', 'South Dakota', 'Connecticut', 'Connecticut', 'District of Columbia', 'Oregon', 'Vermont', 'South Dakota', 'New Jersey', 'Washington', 'Washington', 'Connecticut', 'New Jersey', 'Massachusetts', 'Iowa', 'Vermont', 'Iowa', 'California', 'South Dakota', 'California', 'Rhode Island', 'South Dakota', 'Oregon', 'California', 'Idaho', 'Oregon', 'Vermont', 'California', 'New Jersey', 'New York', 'Massachusetts', 'Rhode Island', 'Iowa', 'Arizona', 'Minnesota', 'Iowa', 'District of Columbia', 'New York', 'Massachusetts', 'New York', 'Arizona', 'North Dakota', 'Iowa', 'Illinois', 'Connecticut', 'Rhode Island', 'Iowa', 'New Hampshire', 'North Dakota', 'Montana', 'District of Columbia', 'Idaho', 'Washington', 'California', 'Idaho', 'Oregon', 'Connecticut', 'New Jersey', 'Vermont', 'Illinois', 'Oregon', 'New York', 'Iowa', 'Vermont', 'Connecticut', 'Rhode Island', 'Illinois', 'Oregon', 'New York', 'Connecticut', 'Illinois', 'New York', 'Oregon', 'Idaho', 'Oregon', 'California', 'Idaho', 'New York', 'Massachusetts', 'Massachusetts', 'Hawaii', 'Connecticut', 'Rhode Island', 'Iowa', 'California', 'Vermont', 'Arizona', 'Washington', 'New Jersey', 'New York', 'Maryland', 'New Jersey', 'Maryland', 'Illinois', 'Alaska', 'Massachusetts', 'Vermont', 'Alaska', 'Maryland', 'Maryland', 'Idaho', 'California', 'Arizona', 'Colorado', 'Vermont', 'Colorado', 'Florida', 'California', 'New York', 'Connecticut', 'Connecticut', 'California', 'Idaho', 'California', 'Iowa', 'California', 'Illinois', 'Illinois', 'Maryland', 'California', 'Idaho', 'Massachusetts', 'Connecticut', 'Maryland', 'Oregon', 'Minnesota', 'Idaho', 'Nevada', 'Iowa', 'District of Columbia', 'Idaho', 'Oregon', 'Massachusetts', 'Montana', 'Maryland', 'Maryland', 'Connecticut', 'Massachusetts', 'Rhode Island', 'Maryland', 'Oregon', 'Florida', 'Oregon', 'Oregon', 'Colorado', 'Florida', 'Colorado', 'Massachusetts', 'Rhode Island', 'Florida', 'Florida', 'Hawaii', 'New York', 'Minnesota', 'Maryland', 'Rhode Island', 'Colorado', 'District of Columbia', 'New Hampshire', 'Illinois', 'Minnesota', 'Oregon', 'Maryland', 'Hawaii', 'New York', 'Maryland', 'Utah', 'South Dakota', 'Oregon', 'Hawaii', 'Illinois', 'Rhode Island', 'New York', 'California', 'Oregon', 'Delaware', 'California', 'Vermont', 'Wyoming', 'Hawaii', 'Oregon', 'Maryland', 'Illinois', 'Illinois', 'Maryland', 'New Mexico', 'Florida', 'Arizona', 'Montana', 'New York', 'Wyoming', 'New York', 'Illinois', 'Florida', 'California', 'Ohio', 'Maryland', 'Illinois', 'Vermont', 'Nebraska', 'Vermont', 'Rhode Island', 'Maryland', 'Rhode Island', 'New Hampshire', 'Oregon', 'Idaho', 'Wyoming', 'Idaho', 'Hawaii', 'Florida', 'New York', 'Florida', 'Washington', 'Connecticut', 'Nebraska', 'Colorado', 'Alaska', 'New York', 'District of Columbia', 'Florida', 'Hawaii', 'Arizona', 'Maryland', 'Rhode Island', 'Ohio', 'Hawaii', 'Arizona', 'Nebraska', 'Hawaii', 'Colorado', 'Montana', 'Colorado', 'Colorado', 'California', 'Florida', 'California', 'Minnesota', 'Idaho', 'Montana', 'Minnesota', 'New York', 'Nebraska', 'New York', 'Rhode Island', 'New York', 'Ohio', 'Minnesota', 'California', 'Delaware', 'Hawaii', 'Montana', 'New York', 'Rhode Island', 'Nebraska', 'New York', 'Minnesota', 'Hawaii', 'Minnesota', 'Maryland', 'Connecticut', 'Connecticut', 'Iowa', 'Minnesota', 'Illinois', 'District of Columbia', 'Rhode Island', 'Wyoming', 'Montana', 'Rhode Island', 'South Dakota', 'Rhode Island', 'California', 'Michigan', 'Michigan', 'Connecticut', 'Vermont', 'Illinois', 'Connecticut', 'Florida', 'California', 'Washington', 'Rhode Island', 'New Hampshire', 'Vermont', 'Alaska', 'Nebraska', 'Vermont', 'Florida', 'District of Columbia', 'Alaska', 'Wyoming', 'Washington', 'Ohio', 'Arizona', 'Maine', 'Hawaii', 'Vermont', 'Alaska', 'Arizona', 'California', 'Alaska', 'South Dakota', 'Florida', 'Alaska', 'New Mexico', 'Connecticut', 'Washington', 'Nebraska', 'Rhode Island', 'Washington', 'Florida', 'New Hampshire', 'Tennessee', 'Oregon', 'Florida', 'New Hampshire', 'Minnesota', 'Florida', 'Pennsylvania', 'Texas', 'Minnesota', 'Iowa', 'California', 'Nebraska', 'Michigan', 'Hawaii', 'Texas', 'Vermont', 'California', 'Hawaii', 'Idaho', 'Utah', 'New Mexico', 'Colorado', 'Montana', 'Montana', 'Montana', 'Texas', 'California', 'Georgia', 'Delaware', 'New York', 'Vermont', 'Illinois', 'California', 'Minnesota', 'Pennsylvania', 'New Hampshire', 'California', 'Texas', 'New Hampshire', 'Iowa', 'Louisiana', 'Tennessee', 'Rhode Island', 'District of Columbia', 'Minnesota', 'Arizona', 'Arizona', 'Arizona', 'Delaware', 'Minnesota', 'United States', 'California', 'Nebraska', 'Oregon', 'New York', 'Hawaii', 'Indiana', 'North Dakota', 'Georgia', 'Alaska', 'Florida', 'North Dakota', 'Arizona', 'California', 'Nevada', 'Montana', 'Hawaii', 'Nebraska', 'New York', 'United States', 'Minnesota', 'Michigan', 'Texas', 'Mississippi', 'South Carolina', 'Florida', 'Illinois', 'California', 'Nebraska', 'Hawaii', 'Florida', 'Florida', 'Ohio', 'Washington', 'Minnesota', 'Pennsylvania', 'Georgia', 'Delaware', 'Nebraska', 'Minnesota', 'New Jersey', 'Delaware', 'New Jersey', 'Illinois', 'Utah', 'Texas', 'Louisiana', 'Iowa', 'New York', 'Pennsylvania', 'Georgia', 'Minnesota', 'Iowa', 'Washington', 'United States', 'New Hampshire', 'Florida', 'Wyoming', 'Colorado', 'Hawaii', 'South Dakota', 'Minnesota', 'New York', 'South Carolina', 'Pennsylvania', 'Minnesota', 'Minnesota', 'United States', 'Pennsylvania', 'Rhode Island', 'Virginia', 'Texas', 'Wisconsin', 'Vermont', 'Texas', 'New York', 'Hawaii', 'Virginia', 'Kansas', 'Michigan', 'Louisiana', 'Minnesota', 'United States', 'Nebraska', 'Georgia', 'Nebraska', 'Virginia', 'Texas', 'Hawaii', 'Virginia', 'New York', 'Utah', 'Rhode Island', 'Utah', 'Colorado', 'North Dakota', 'New Hampshire', 'Michigan', 'United States', 'New Jersey', 'Delaware', 'United States', 'Delaware', 'Virginia', 'Mississippi', 'Minnesota', 'Louisiana', 'Maine', 'Colorado', 'North Carolina', 'United States', 'Delaware', 'Arizona', 'Montana', 'New York', 'Hawaii', 'New York', 'Florida', 'Alaska', 'Colorado', 'Texas', 'North Dakota', 'Texas', 'Texas', 'Ohio', 'Pennsylvania', 'North Dakota', 'Wisconsin', 'Pennsylvania', 'Michigan', 'Virginia', 'South Carolina', 'Hawaii', 'Georgia', 'Pennsylvania', 'Michigan', 'Virginia', 'Nebraska', 'Hawaii', 'Arizona', 'Florida', 'New York', 'Tennessee', 'Rhode Island', 'South Dakota', 'Hawaii', 'New York', 'Rhode Island', 'New Hampshire', 'Rhode Island', 'Iowa', 'Minnesota', 'Hawaii', 'New Hampshire', 'Georgia', 'Virginia', 'Louisiana', 'North Carolina', 'Nebraska', 'Ohio', 'Kansas', 'Louisiana', 'Minnesota', 'Iowa', 'Rhode Island', 'Michigan', 'New Hampshire', 'Virginia', 'Oregon', 'Montana', 'North Dakota', 'Hawaii', 'Idaho', 'Maryland', 'Montana', 'Utah', 'New Mexico', 'Montana', 'New Hampshire', 'Delaware', 'Indiana', 'Ohio', 'Idaho', 'New Jersey', 'United States', 'Georgia', 'Indiana', 'Idaho', 'New York', 'Wisconsin', 'Nebraska', 'Delaware', 'South Carolina', 'Texas', 'Maine', 'Maryland', 'Minnesota', 'New Hampshire', 'New Hampshire', 'New Hampshire', 'New York', 'Colorado', 'Florida', 'Delaware', 'Washington', 'Alabama', 'Alabama', 'Ohio', 'Mississippi', 'Nevada', 'Arizona', 'Georgia', 'Connecticut', 'South Carolina', 'Vermont', 'Maryland', 'District of Columbia', 'Utah', 'Delaware', 'Rhode Island', 'North Dakota', 'Colorado', 'Maryland', 'Hawaii', 'Colorado', 'Nebraska', 'Louisiana', 'District of Columbia', 'Texas', 'New Hampshire', 'Alabama', 'Ohio', 'Alabama', 'Michigan', 'North Carolina', 'South Carolina', 'Iowa', 'Wyoming', 'New York', 'New Hampshire', 'Utah', 'Alaska', 'Iowa', 'Washington', 'South Carolina', 'Texas', 'Wisconsin', 'Minnesota', 'Kentucky', 'North Carolina', 'Wisconsin', 'Nebraska', 'Georgia', 'United States', 'Maine', 'Hawaii', 'Connecticut', 'New Mexico', 'Maine', 'Utah', 'Tennessee', 'Wyoming', 'Virginia', 'Alabama', 'Rhode Island', 'Virginia', 'Indiana', 'Oregon', 'Georgia', 'Nebraska', 'Mississippi', 'Texas', 'Georgia', 'Delaware', 'Wisconsin', 'Georgia', 'Idaho', 'Hawaii', 'South Carolina', 'New Hampshire', 'North Dakota', 'Tennessee', 'Tennessee', 'Hawaii', 'New Hampshire', 'Pennsylvania', 'Texas', 'United States', 'Nebraska', 'Missouri', 'Michigan', 'South Carolina', 'North Carolina', 'Connecticut', 'Virginia', 'Hawaii', 'Maine', 'South Carolina', 'Connecticut', 'Colorado', 'Maryland', 'District of Columbia', 'New York', 'Wisconsin', 'Wisconsin', 'Rhode Island', 'Arizona', 'Wyoming', 'Oklahoma', 'Nebraska', 'Mississippi', 'New Hampshire', 'Indiana', 'Texas', 'Pennsylvania', 'North Carolina', 'Wisconsin', 'Indiana', 'Louisiana', 'Washington', 'Georgia', 'Hawaii', 'Iowa', 'Delaware', 'Alaska', 'Iowa', 'Louisiana', 'Montana', 'Montana', 'Maine', 'Maryland', 'Maine', 'Tennessee', 'New York', 'South Carolina', 'North Dakota', 'New Mexico', 'Utah', 'New Hampshire', 'Rhode Island', 'North Carolina', 'Wisconsin', 'Colorado', 'Minnesota', 'North Carolina', 'New Hampshire', 'New Jersey', 'New Hampshire', 'Louisiana', 'Georgia', 'North Carolina', 'Oregon', 'Louisiana', 'South Carolina', 'District of Columbia', 'Idaho', 'Connecticut', 'Colorado', 'Maryland', 'Florida', 'Maryland', 'Hawaii', 'Nebraska', 'Connecticut', 'Connecticut', 'Nebraska', 'Minnesota', 'West Virginia', 'New Jersey', 'Minnesota', 'Iowa', 'Kansas', 'Connecticut', 'United States', 'Maine', 'Indiana', 'Kentucky', 'District of Columbia', 'Missouri', 'Wyoming', 'Hawaii', 'Florida', 'New York', 'District of Columbia', 'Arizona', 'Florida', 'North Carolina', 'Florida', 'Montana', 'Florida', 'Hawaii', 'Tennessee', 'Kansas', 'Texas', 'Vermont', 'Ohio', 'Mississippi', 'Pennsylvania', 'North Carolina', 'Michigan', 'Ohio', 'Minnesota', 'Rhode Island', 'Mississippi', 'Connecticut', 'New Mexico', 'Nebraska', 'Maryland', 'Tennessee', 'New York', 'Wyoming', 'Minnesota', 'Nebraska', 'Minnesota', 'North Carolina', 'Vermont', 'Rhode Island', 'Indiana', 'South Dakota', 'United States', 'Washington', 'North Dakota', 'North Carolina', 'Colorado', 'Colorado', 'Nebraska', 'Ohio', 'Louisiana', 'Maine', 'Connecticut', 'New Hampshire', 'Maryland', 'Rhode Island', 'Utah', 'Idaho', 'Oklahoma', 'Florida', 'Idaho', 'Washington', 'Louisiana', 'Missouri', 'Maine', 'Maine', 'Alabama', 'Wisconsin', 'Washington', 'Alabama', 'Pennsylvania', 'District of Columbia', 'Hawaii', 'Louisiana', 'New York', 'Colorado', 'Alaska', 'Hawaii', 'Minnesota', 'Arizona', 'Wyoming', 'North Dakota', 'Alaska', 'District of Columbia', 'Alabama', 'Mississippi', 'Hawaii', 'Louisiana', 'Missouri', 'Virginia', 'New Jersey', 'Minnesota', 'Florida', 'Texas', 'New Mexico', 'Wisconsin', 'Michigan', 'Minnesota', 'Michigan', 'Vermont', 'Alaska', 'South Dakota', 'Delaware', 'Maine', 'Missouri', 'Nebraska', 'Maryland', 'Indiana', 'Rhode Island', 'Tennessee', 'Texas', 'New Mexico', 'Minnesota', 'New Mexico', 'Arkansas', 'Delaware', 'Missouri', 'Florida', 'Oregon', 'Indiana', 'United States', 'Iowa', 'Ohio', 'United States', 'Georgia', 'Alaska', 'North Carolina', 'New York', 'New Hampshire', 'Hawaii', 'New Jersey', 'Alabama', 'Virginia', 'Texas', 'Delaware', 'Alaska', 'Mississippi', 'Utah', 'Virginia', 'South Dakota', 'Tennessee', 'Wisconsin', ...]
In [10]:
state=df["State"].to_frame() # seçtiğimiz bir sütunu dataframe türüne çevirme ve bir değişkene atama işlemi
state
Out[10]:
| State | |
|---|---|
| 0 | Vermont |
| 1 | Vermont |
| 2 | Vermont |
| 3 | District of Columbia |
| 4 | Arizona |
| 5 | District of Columbia |
| 6 | South Dakota |
| 7 | Vermont |
| 8 | Vermont |
| 9 | Arizona |
| 10 | District of Columbia |
| 11 | Washington |
| 12 | District of Columbia |
| 13 | District of Columbia |
| 14 | Washington |
| 15 | District of Columbia |
| 16 | District of Columbia |
| 17 | Washington |
| 18 | Vermont |
| 19 | Washington |
| 20 | Washington |
| 21 | Vermont |
| 22 | District of Columbia |
| 23 | Arizona |
| 24 | District of Columbia |
| 25 | South Dakota |
| 26 | Arizona |
| 27 | District of Columbia |
| 28 | Arizona |
| 29 | Arizona |
| … | … |
| 10266 | West Virginia |
| 10267 | Mississippi |
| 10268 | West Virginia |
| 10269 | Alabama |
| 10270 | Alabama |
| 10271 | West Virginia |
| 10272 | Kentucky |
| 10273 | Alabama |
| 10274 | Louisiana |
| 10275 | Louisiana |
| 10276 | Alabama |
| 10277 | Kentucky |
| 10278 | West Virginia |
| 10279 | West Virginia |
| 10280 | Louisiana |
| 10281 | Alabama |
| 10282 | Louisiana |
| 10283 | Alabama |
| 10284 | Louisiana |
| 10285 | Louisiana |
| 10286 | Mississippi |
| 10287 | Mississippi |
| 10288 | District of Columbia |
| 10289 | Mississippi |
| 10290 | Mississippi |
| 10291 | District of Columbia |
| 10292 | Mississippi |
| 10293 | Mississippi |
| 10294 | District of Columbia |
| 10295 | District of Columbia |
10296 rows × 1 columns
In [13]:
state.to_csv("State.csv") # to_csv metodu sayesinde dataframe değişkenimizi bir csv dosyasına dönüştürüp
# içinde bulunduğumuz klasöre kaydedebiliyoruz.
In [14]:
pd.read_csv("State.csv") # kaydettiğimiz dosyada iki tane index sütunu oluştuğu görülmektedir.
# Bunu engellemek için aşağıdaki yolu izlememiz gerekiyor.
Out[14]:
| Unnamed: 0 | State | |
|---|---|---|
| 0 | 0 | Vermont |
| 1 | 1 | Vermont |
| 2 | 2 | Vermont |
| 3 | 3 | District of Columbia |
| 4 | 4 | Arizona |
| 5 | 5 | District of Columbia |
| 6 | 6 | South Dakota |
| 7 | 7 | Vermont |
| 8 | 8 | Vermont |
| 9 | 9 | Arizona |
| 10 | 10 | District of Columbia |
| 11 | 11 | Washington |
| 12 | 12 | District of Columbia |
| 13 | 13 | District of Columbia |
| 14 | 14 | Washington |
| 15 | 15 | District of Columbia |
| 16 | 16 | District of Columbia |
| 17 | 17 | Washington |
| 18 | 18 | Vermont |
| 19 | 19 | Washington |
| 20 | 20 | Washington |
| 21 | 21 | Vermont |
| 22 | 22 | District of Columbia |
| 23 | 23 | Arizona |
| 24 | 24 | District of Columbia |
| 25 | 25 | South Dakota |
| 26 | 26 | Arizona |
| 27 | 27 | District of Columbia |
| 28 | 28 | Arizona |
| 29 | 29 | Arizona |
| … | … | … |
| 10266 | 10266 | West Virginia |
| 10267 | 10267 | Mississippi |
| 10268 | 10268 | West Virginia |
| 10269 | 10269 | Alabama |
| 10270 | 10270 | Alabama |
| 10271 | 10271 | West Virginia |
| 10272 | 10272 | Kentucky |
| 10273 | 10273 | Alabama |
| 10274 | 10274 | Louisiana |
| 10275 | 10275 | Louisiana |
| 10276 | 10276 | Alabama |
| 10277 | 10277 | Kentucky |
| 10278 | 10278 | West Virginia |
| 10279 | 10279 | West Virginia |
| 10280 | 10280 | Louisiana |
| 10281 | 10281 | Alabama |
| 10282 | 10282 | Louisiana |
| 10283 | 10283 | Alabama |
| 10284 | 10284 | Louisiana |
| 10285 | 10285 | Louisiana |
| 10286 | 10286 | Mississippi |
| 10287 | 10287 | Mississippi |
| 10288 | 10288 | District of Columbia |
| 10289 | 10289 | Mississippi |
| 10290 | 10290 | Mississippi |
| 10291 | 10291 | District of Columbia |
| 10292 | 10292 | Mississippi |
| 10293 | 10293 | Mississippi |
| 10294 | 10294 | District of Columbia |
| 10295 | 10295 | District of Columbia |
10296 rows × 2 columns
In [15]:
state.to_csv("State.csv",index=False) # index=False parametresi sayesinde fazladan oluşturulan index sütunundan kurtulabiliriz.
In [16]:
pd.read_csv("State.csv")
Out[16]:
| State | |
|---|---|
| 0 | Vermont |
| 1 | Vermont |
| 2 | Vermont |
| 3 | District of Columbia |
| 4 | Arizona |
| 5 | District of Columbia |
| 6 | South Dakota |
| 7 | Vermont |
| 8 | Vermont |
| 9 | Arizona |
| 10 | District of Columbia |
| 11 | Washington |
| 12 | District of Columbia |
| 13 | District of Columbia |
| 14 | Washington |
| 15 | District of Columbia |
| 16 | District of Columbia |
| 17 | Washington |
| 18 | Vermont |
| 19 | Washington |
| 20 | Washington |
| 21 | Vermont |
| 22 | District of Columbia |
| 23 | Arizona |
| 24 | District of Columbia |
| 25 | South Dakota |
| 26 | Arizona |
| 27 | District of Columbia |
| 28 | Arizona |
| 29 | Arizona |
| … | … |
| 10266 | West Virginia |
| 10267 | Mississippi |
| 10268 | West Virginia |
| 10269 | Alabama |
| 10270 | Alabama |
| 10271 | West Virginia |
| 10272 | Kentucky |
| 10273 | Alabama |
| 10274 | Louisiana |
| 10275 | Louisiana |
| 10276 | Alabama |
| 10277 | Kentucky |
| 10278 | West Virginia |
| 10279 | West Virginia |
| 10280 | Louisiana |
| 10281 | Alabama |
| 10282 | Louisiana |
| 10283 | Alabama |
| 10284 | Louisiana |
| 10285 | Louisiana |
| 10286 | Mississippi |
| 10287 | Mississippi |
| 10288 | District of Columbia |
| 10289 | Mississippi |
| 10290 | Mississippi |
| 10291 | District of Columbia |
| 10292 | Mississippi |
| 10293 | Mississippi |
| 10294 | District of Columbia |
| 10295 | District of Columbia |
10296 rows × 1 columns
In [19]:
import os # os metodunu kullanmak için önce import etmemiz lazım
os.chdir("C:/Users/E-A-S/Desktop/pandas-kaynak") # excel dosyamızın olduğu dizini dosyaların aranacağı dizinler arasına eklemek için yapıyoruz.
pd.read_excel("excel.xlsx") # excel dosyası okuma işlemi
Out[19]:
| Futbolcu | Gol | |
|---|---|---|
| 0 | Miroslav Klose | 5 |
| 1 | Lucas Podolski | 4 |
| 2 | David Villa | 3 |
| 3 | Maxi Rodriguez | 3 |
| 4 | Thierry Henry | 3 |
| 5 | Zinedine Zidane | 3 |
| 6 | Hernan Crespo | 3 |
| 7 | Fernando Torres | 3 |
| 8 | Ronaldo | 3 |
| 9 | Adriano | 2 |
In [29]:
pd.read_excel("excel.xlsx", sheet_name=["2006","2010"]) # excel dosyamızda iki sayfa bulunmaktadır.
# 2006 ve 2010 adındaki iki sayfayı okumak için de sheet_name parametresi kullanıldı.
# Kaç sayfa olursa olsun hangi sayfaları okumak istiyorsak sadece onları belirtebiliriz.
Out[29]:
OrderedDict([('2006', Futbolcu Gol
0 Miroslav Klose 5
1 Lucas Podolski 4
2 David Villa 3
3 Maxi Rodriguez 3
4 Thierry Henry 3
5 Zinedine Zidane 3
6 Hernan Crespo 3
7 Fernando Torres 3
8 Ronaldo 3
9 Adriano 2), ('2010', Futbolcu Gol
0 David Villa 5
1 Wesley Sneijder 5
2 Diego Forlan 5
3 Thomas Müller 5
4 Miroslav Klose 4
5 Robert Vittek 4
6 G.Higuain 4
7 Asamoah Gyan 3
8 Landon Donovan 3
9 Luis Suares 3)])
In [30]:
c=pd.read_excel("excel.xlsx", sheet_name=["2006","2010"])
c["2010"] # okuduğumuz excel dosyası sayfalarından hangi sayfayı görmek istersek onu seçebiliriz.
Out[30]:
| Futbolcu | Gol | |
|---|---|---|
| 0 | David Villa | 5 |
| 1 | Wesley Sneijder | 5 |
| 2 | Diego Forlan | 5 |
| 3 | Thomas Müller | 5 |
| 4 | Miroslav Klose | 4 |
| 5 | Robert Vittek | 4 |
| 6 | G.Higuain | 4 |
| 7 | Asamoah Gyan | 3 |
| 8 | Landon Donovan | 3 |
| 9 | Luis Suares | 3 |
In [31]:
pd.read_csv("2014 wc.csv") # bir csv dosyası okuma işlemi
Out[31]:
| Futbolcu | Gol | |
|---|---|---|
| 0 | James Rodriguez | 6 |
| 1 | Thomas Müller | 5 |
| 2 | Neymar | 4 |
| 3 | R.Van Persie | 4 |
| 4 | Lionel Messi | 4 |
| 5 | Andre Schürrle | 3 |
| 6 | Enner Valencia | 3 |
| 7 | Shakiri | 3 |
| 8 | Arjen Robben | 3 |
| 9 | Tim Cahill | 2 |
In [34]:
çevirme=pd.read_csv("2014 wc.csv")
çevirme.to_excel("2014.xlsx", sheet_name="24",index=False) # burada okuduğumuz bir excel dosyasını csv dosyasına çevirme
# işlemi yapılmaktadır. Okunan csv dosyası hangi dizindeyse bu kodlar sayesinde 2014.xlsx adında bir exel dosyasına
# çevrilmiş ve kaydedilmiştir. Eski csv dosyası da hala durmaktadır. Oluşturulan excel dosyasında bu veriler 24 adında bir
# sayfaya kaydedilmiştir. index?False parametresi sayesinde de fazladan bir index sütunu oluşturulması engellenmiştir.
In [35]:
pd.read_excel("2014.xlsx")
Out[35]:
| Futbolcu | Gol | |
|---|---|---|
| 0 | James Rodriguez | 6 |
| 1 | Thomas Müller | 5 |
| 2 | Neymar | 4 |
| 3 | R.Van Persie | 4 |
| 4 | Lionel Messi | 4 |
| 5 | Andre Schürrle | 3 |
| 6 | Enner Valencia | 3 |
| 7 | Shakiri | 3 |
| 8 | Arjen Robben | 3 |
| 9 | Tim Cahill | 2 |
In [45]:
a=pd.read_html("https://ipfs.io/ipfs/QmXoypizjW3WknFiJnKLwHCnL72vedxjQkDDP1mXWo6uco/wiki/List_of_banks_in_Turkey.html")
a # internetten direk html sayfası okuma.
Out[45]:
[ 0 1 \
0 Bank Foundation
1 Ziraat Bankası 1863
2 Türkiye İş Bankası 1924
3 Garanti Bank 1946
4 Akbank 1948
5 Yapı ve Kredi Bankası 1944
6 Halk Bankası 1938
7 VakıfBank 1954
8 Finansbank 1987
9 Türk Ekonomi Bankası 1927
10 Denizbank 1997
11 HSBC Bank 1990
12 ING Bank 1984
13 Türk Eximbank 1987
14 Odeabank 2012
15 Şekerbank 1953
16 İller Bankası 1933
17 Türkiye Sınai Kalkınma Bankası 1950
18 Alternatif Bank 1992
19 Citibank 1980
20 Anadolubank 1996
21 Burgan Bank 1992
22 İstanbul Takas ve Saklama Bankası 1995
23 Tekstilbank 1986
24 Deutsche Bank 1988
25 Fibabanka 1984
26 Aktif Yatırım Bankası 1999
27 The Royal Bank of Scotland 1921
28 Türkiye Kalkınma Bankası 1975
29 Turkland Bank 1991
30 Bank of Tokyo-Mitsubishi UFJ Turkey 2013
31 Arap Türk Bankası 1977
32 Intesa Sanpaolo S.p.A. 2013
33 Merrill Lynch 1992
34 BankPozitif 1999
35 Société Générale 1989
36 Turkish Bank 1982
37 Rabobank 2014
38 JPMorgan Chase 1984
39 Birleşik Fon Bankası 1958
40 Bank Mellat 1982
41 Nurol Yatırım Bankası 1999
42 Diler Yatırım Bankası 1998
43 GSD Yatırım Bankası 1998
44 Habib Bank Limited 1983
45 Credit Agricole (Standard Chartered) 1990
46 Adabank 1985
47 Taib Yatırım Bank (Pasha Yatırım) 1987
2 \
0 # of branches As of 22 February 2016
1 1789
2 1355
3 974
4 901
5 997
6 948
7 917
8 641
9 528
10 691
11 280
12 288
13 3
14 55
15 300
16 19
17 3
18 57
19 8
20 106
21 55
22 1
23 44
24 1
25 68
26 8
27 1
28 1
29 34
30 1
31 7
32 1
33 1
34 1
35 1
36 13
37 1
38 1
39 1
40 3
41 1
42 1
43 1
44 1
45 1
46 1
47 1
3
0 Total assets (million TL) As of September 2015[4]
1 299084
2 279942
3 260700
4 236148
5 232248
6 189686
7 188820
8 90410
9 73666
10 82195
11 32869
12 51645
13 46518
14 32841
15 23777
16 17494
17 20853
18 13793
19 8920
20 11620
21 10696
22 7631
23 6264
24 2681
25 9476
26 7383
27 1754
28 4975
29 5651
30 4737
31 3975
32 3925
33 204
34 2090
35 477
36 1397
37 843
38 392
39 2450
40 354
41 788
42 116
43 231
44 113
45 83
46 53
47 276 ,
0 \
0 Banks of Turkey
1 NaN
2 Central Bank
3 NaN
4 Commercial
5 Public
6 NaN
7 Private
8 NaN
9 Foreign
10 NaN
11 Investment
12 Public
13 NaN
14 Participation
15 Public
16 NaN
17 Private
18 NaN
19 1 Under TMSF italic: Global branch page
20 Public
21 NaN
22 Private
23 NaN
24 Foreign
25 Public
26 Public
27 NaN
28 Private
1 2 \
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 Public Halk Bankası VakıfBank Ziraat Bankas... Public
5 Halk Bankası VakıfBank Ziraat Bankası Adaba... NaN
6 NaN NaN
7 Akbank Anadolubank Türkiye İş Bankası Fibab... NaN
8 NaN NaN
9 A&T Bank Abank Burgan Bank Citibank DenizB... NaN
10 NaN NaN
11 Public İlbank Public
12 İlbank NaN
13 NaN NaN
14 Public Vakıf Katılım Ziraat Katılım Private ... Public
15 Vakıf Katılım Ziraat Katılım NaN
16 NaN NaN
17 Albaraka Türk Kuveyt Türk Türkiye Finans NaN
18 NaN NaN
19 NaN NaN
20 Halk Bankası VakıfBank Ziraat Bankası Adaba... NaN
21 NaN NaN
22 Akbank Anadolubank Türkiye İş Bankası Fibab... NaN
23 NaN NaN
24 A&T Bank Abank Burgan Bank Citibank DenizB... NaN
25 İlbank NaN
26 Vakıf Katılım Ziraat Katılım NaN
27 NaN NaN
28 Albaraka Türk Kuveyt Türk Türkiye Finans NaN
3 4 5 \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 Halk Bankası VakıfBank Ziraat Bankası Adaba... NaN Private
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
9 NaN NaN NaN
10 NaN NaN NaN
11 İlbank NaN NaN
12 NaN NaN NaN
13 NaN NaN NaN
14 Vakıf Katılım Ziraat Katılım NaN Private
15 NaN NaN NaN
16 NaN NaN NaN
17 NaN NaN NaN
18 NaN NaN NaN
19 NaN NaN NaN
20 NaN NaN NaN
21 NaN NaN NaN
22 NaN NaN NaN
23 NaN NaN NaN
24 NaN NaN NaN
25 NaN NaN NaN
26 NaN NaN NaN
27 NaN NaN NaN
28 NaN NaN NaN
6 7 8 \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 Akbank Anadolubank Türkiye İş Bankası Fibab... NaN Foreign
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
9 NaN NaN NaN
10 NaN NaN NaN
11 NaN NaN NaN
12 NaN NaN NaN
13 NaN NaN NaN
14 Albaraka Türk Kuveyt Türk Türkiye Finans NaN NaN
15 NaN NaN NaN
16 NaN NaN NaN
17 NaN NaN NaN
18 NaN NaN NaN
19 NaN NaN NaN
20 NaN NaN NaN
21 NaN NaN NaN
22 NaN NaN NaN
23 NaN NaN NaN
24 NaN NaN NaN
25 NaN NaN NaN
26 NaN NaN NaN
27 NaN NaN NaN
28 NaN NaN NaN
9
0 NaN
1 NaN
2 NaN
3 NaN
4 A&T Bank Abank Burgan Bank Citibank DenizB...
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
17 NaN
18 NaN
19 NaN
20 NaN
21 NaN
22 NaN
23 NaN
24 NaN
25 NaN
26 NaN
27 NaN
28 NaN ,
0 1
0 Public Halk Bankası VakıfBank Ziraat Bankası Adaba...
1 NaN NaN
2 Private Akbank Anadolubank Türkiye İş Bankası Fibab...
3 NaN NaN
4 Foreign A&T Bank Abank Burgan Bank Citibank DenizB...,
0 1
0 Public İlbank,
0 1
0 Public Vakıf Katılım Ziraat Katılım
1 NaN NaN
2 Private Albaraka Türk Kuveyt Türk Türkiye Finans,
0 \
0 List of banks in Asia
1 NaN
2 Sovereign states
3 NaN
4 States withlimited recognition
5 NaN
6 Dependencies andother territories
1
0 NaN
1 NaN
2 Afghanistan Armenia Azerbaijan Bahrain Ban...
3 NaN
4 Abkhazia Nagorno-Karabakh Northern Cyprus P...
5 NaN
6 British Indian Ocean Territory Christmas Isla... ,
0 \
0 List of banks in Europe
1 NaN
2 Sovereign states
3 NaN
4 States with limitedrecognition
5 NaN
6 Dependencies andother territories
1
0 NaN
1 NaN
2 Albania Andorra Armenia Austria Azerbaijan...
3 NaN
4 Abkhazia Kosovo Nagorno-Karabakh Northern C...
5 NaN
6 Åland Faroe Islands Gibraltar Guernsey Isl... ]
In [48]:
a[0] # html sayfası içindeki tabloyu aldık.
Out[48]:
| 0 | 1 | 2 | 3 | |
|---|---|---|---|---|
| 0 | Bank | Foundation | # of branches As of 22 February 2016 | Total assets (million TL) As of September 2015[4] |
| 1 | Ziraat Bankası | 1863 | 1789 | 299084 |
| 2 | Türkiye İş Bankası | 1924 | 1355 | 279942 |
| 3 | Garanti Bank | 1946 | 974 | 260700 |
| 4 | Akbank | 1948 | 901 | 236148 |
| 5 | Yapı ve Kredi Bankası | 1944 | 997 | 232248 |
| 6 | Halk Bankası | 1938 | 948 | 189686 |
| 7 | VakıfBank | 1954 | 917 | 188820 |
| 8 | Finansbank | 1987 | 641 | 90410 |
| 9 | Türk Ekonomi Bankası | 1927 | 528 | 73666 |
| 10 | Denizbank | 1997 | 691 | 82195 |
| 11 | HSBC Bank | 1990 | 280 | 32869 |
| 12 | ING Bank | 1984 | 288 | 51645 |
| 13 | Türk Eximbank | 1987 | 3 | 46518 |
| 14 | Odeabank | 2012 | 55 | 32841 |
| 15 | Şekerbank | 1953 | 300 | 23777 |
| 16 | İller Bankası | 1933 | 19 | 17494 |
| 17 | Türkiye Sınai Kalkınma Bankası | 1950 | 3 | 20853 |
| 18 | Alternatif Bank | 1992 | 57 | 13793 |
| 19 | Citibank | 1980 | 8 | 8920 |
| 20 | Anadolubank | 1996 | 106 | 11620 |
| 21 | Burgan Bank | 1992 | 55 | 10696 |
| 22 | İstanbul Takas ve Saklama Bankası | 1995 | 1 | 7631 |
| 23 | Tekstilbank | 1986 | 44 | 6264 |
| 24 | Deutsche Bank | 1988 | 1 | 2681 |
| 25 | Fibabanka | 1984 | 68 | 9476 |
| 26 | Aktif Yatırım Bankası | 1999 | 8 | 7383 |
| 27 | The Royal Bank of Scotland | 1921 | 1 | 1754 |
| 28 | Türkiye Kalkınma Bankası | 1975 | 1 | 4975 |
| 29 | Turkland Bank | 1991 | 34 | 5651 |
| 30 | Bank of Tokyo-Mitsubishi UFJ Turkey | 2013 | 1 | 4737 |
| 31 | Arap Türk Bankası | 1977 | 7 | 3975 |
| 32 | Intesa Sanpaolo S.p.A. | 2013 | 1 | 3925 |
| 33 | Merrill Lynch | 1992 | 1 | 204 |
| 34 | BankPozitif | 1999 | 1 | 2090 |
| 35 | Société Générale | 1989 | 1 | 477 |
| 36 | Turkish Bank | 1982 | 13 | 1397 |
| 37 | Rabobank | 2014 | 1 | 843 |
| 38 | JPMorgan Chase | 1984 | 1 | 392 |
| 39 | Birleşik Fon Bankası | 1958 | 1 | 2450 |
| 40 | Bank Mellat | 1982 | 3 | 354 |
| 41 | Nurol Yatırım Bankası | 1999 | 1 | 788 |
| 42 | Diler Yatırım Bankası | 1998 | 1 | 116 |
| 43 | GSD Yatırım Bankası | 1998 | 1 | 231 |
| 44 | Habib Bank Limited | 1983 | 1 | 113 |
| 45 | Credit Agricole (Standard Chartered) | 1990 | 1 | 83 |
| 46 | Adabank | 1985 | 1 | 53 |
| 47 | Taib Yatırım Bank (Pasha Yatırım) | 1987 | 1 | 276 |
In [ ]: