अभी देखें इस ट्यूटोरियल में रियल पायथन टीम द्वारा बनाया गया एक संबंधित वीडियो कोर्स है। अपनी समझ को गहरा करने के लिए इसे लिखित ट्यूटोरियल के साथ देखें:पंडों के साथ फ़ाइलें पढ़ना और लिखना
पंडों एक शक्तिशाली और लचीला पायथन पैकेज है जो आपको लेबल और समय श्रृंखला डेटा के साथ काम करने की अनुमति देता है। यह आँकड़ों के तरीके भी प्रदान करता है, प्लॉटिंग को सक्षम बनाता है, और बहुत कुछ। पंडों की एक महत्वपूर्ण विशेषता एक्सेल, सीएसवी और कई अन्य प्रकार की फाइलों को लिखने और पढ़ने की क्षमता है। पंडों जैसे कार्य read_csv() विधि आपको फाइलों के साथ प्रभावी ढंग से काम करने में सक्षम बनाती है। आप पंडों की वस्तुओं से डेटा और लेबल को एक फ़ाइल में सहेजने के लिए उनका उपयोग कर सकते हैं और बाद में उन्हें पंडों के रूप में लोड कर सकते हैं Series या DataFrame उदाहरण।
इस ट्यूटोरियल में, आप सीखेंगे:
- क्या Pandas IO टूल एपीआई है
- कैसे डेटा पढ़ें और लिखें फ़ाइलों के लिए और से
- विभिन्न फ़ाइल स्वरूपों के साथ कैसे कार्य करें
- बड़े डेटा के साथ कैसे कार्य करें कुशलता से
आइए फ़ाइलें पढ़ना और लिखना शुरू करें!
मुफ़्त बोनस: पायथन मास्टरी पर 5 विचार, पायथन डेवलपर्स के लिए एक नि:शुल्क पाठ्यक्रम जो आपको रोडमैप और मानसिकता दिखाता है कि आपको अपने पायथन कौशल को अगले स्तर तक ले जाने की आवश्यकता होगी।
पंडों को स्थापित करना
इस ट्यूटोरियल में कोड CPython 3.7.4 और पांडा 0.25.1 के साथ निष्पादित किया गया है। यह सुनिश्चित करना फायदेमंद होगा कि आपके पास अपनी मशीन पर पायथन और पंडों के नवीनतम संस्करण हैं। हो सकता है कि आप एक नया आभासी वातावरण बनाना चाहें और इस ट्यूटोरियल के लिए निर्भरताएँ स्थापित करना चाहें।
सबसे पहले, आपको पंडों के पुस्तकालय की आवश्यकता होगी। आपने शायद इसे पहले ही इंस्टाल कर लिया है। यदि आप नहीं करते हैं, तो आप इसे पाइप के साथ स्थापित कर सकते हैं:
$ pip install pandas
एक बार इंस्टॉलेशन प्रक्रिया पूरी हो जाने के बाद, आपके पास पंडों को स्थापित और तैयार होना चाहिए।
एनाकोंडा एक उत्कृष्ट पायथन वितरण है जो पायथन के साथ आता है, पंडों जैसे कई उपयोगी पैकेज, और एक पैकेज और पर्यावरण प्रबंधक जिसे कोंडा कहा जाता है। एनाकोंडा के बारे में अधिक जानने के लिए, विंडोज़ पर मशीन लर्निंग के लिए पायथन की स्थापना देखें।
यदि आपके आभासी वातावरण में पांडा नहीं हैं, तो आप इसे कोंडा के साथ स्थापित कर सकते हैं:
$ conda install pandas
कोंडा शक्तिशाली है क्योंकि यह निर्भरताओं और उनके संस्करणों का प्रबंधन करता है। कोंडा के साथ काम करने के बारे में अधिक जानने के लिए, आप आधिकारिक दस्तावेज देख सकते हैं।
डेटा तैयार करना
इस ट्यूटोरियल में, आप 20 देशों से संबंधित डेटा का उपयोग करेंगे। यहां उन डेटा और स्रोतों का अवलोकन दिया गया है जिनके साथ आप काम करेंगे:
-
देश देश के नाम से जाना जाता है। प्रत्येक देश जनसंख्या, क्षेत्रफल या सकल घरेलू उत्पाद (जीडीपी) के लिए शीर्ष 10 की सूची में है। डेटासेट के लिए पंक्ति लेबल ISO 3166-1 में परिभाषित तीन-अक्षर वाले देश कोड हैं। डेटासेट के लिए कॉलम लेबल
COUNTRYहै । -
जनसंख्या लाखों में व्यक्त किया गया है। डेटा विकिपीडिया पर जनसंख्या द्वारा देशों और निर्भरताओं की सूची से आता है। डेटासेट के लिए कॉलम लेबल
POPहै । -
क्षेत्र हजारों किलोमीटर वर्ग में व्यक्त किया गया है। डेटा विकिपीडिया पर क्षेत्र के अनुसार देशों और निर्भरताओं की सूची से आता है। डेटासेट के लिए कॉलम लेबल
AREAहै । -
सकल घरेलू उत्पाद 2017 के लिए संयुक्त राष्ट्र के आंकड़ों के अनुसार, लाखों अमेरिकी डॉलर में व्यक्त किया गया है। आप विकिपीडिया पर नाममात्र जीडीपी द्वारा देशों की सूची में यह डेटा पा सकते हैं। डेटासेट के लिए कॉलम लेबल
GDPहै । -
महाद्वीप अफ्रीका, एशिया, ओशिनिया, यूरोप, उत्तरी अमेरिका या दक्षिण अमेरिका है। यह जानकारी आपको विकिपीडिया पर भी मिल जाएगी। डेटासेट के लिए कॉलम लेबल
CONTहै । -
स्वतंत्रता दिवस एक तारीख है जो एक राष्ट्र की स्वतंत्रता की याद दिलाती है। डेटा विकिपीडिया पर राष्ट्रीय स्वतंत्रता दिवसों की सूची से आता है। दिनांक आईएसओ 8601 प्रारूप में दिखाए गए हैं। पहले चार अंक वर्ष का प्रतिनिधित्व करते हैं, अगले दो अंक महीने हैं, और अंतिम दो महीने के दिन के लिए हैं। डेटासेट के लिए कॉलम लेबल
IND_DAYहै ।
तालिका के रूप में डेटा इस प्रकार दिखता है:
| देश | POP | क्षेत्र | जीडीपी | CONT | IND_DAY | |
|---|---|---|---|---|---|---|
| CHN | चीन | 1398.72 | 9596.96 | 12234.78 | एशिया | |
| IND | भारत | 1351.16 | 3287.26 | 2575.67 | एशिया | 1947-08-15 |
| USA | US | 329.74 | 9833.52 | 19485.39 | N.America | 1776-07-04 |
| IDN | इंडोनेशिया | 268.07 | 1910.93 | 1015.54 | एशिया | 1945-08-17 |
| BRA | ब्राज़ील | 210.32 | 8515.77 | 2055.51 | S.America | 1822-09-07 |
| PAK | पाकिस्तान | 205.71 | 881.91 | 302.14 | एशिया | 1947-08-14 |
| NGA | नाइजीरिया | 200.96 | 923.77 | 375.77 | अफ्रीका | 1960-10-01 |
| BGD | बांग्लादेश | 167.09 | 147.57 | 245.63 | एशिया | 1971-03-26 |
| RUS | रूस | 146.79 | 17098.25 | 1530.75 | 1992-06-12 | |
| MEX | मेक्सिको | 126.58 | 1964.38 | 1158.23 | N.America | 1810-09-16 |
| JPN | जापान | 126.22 | 377.97 | 4872.42 | एशिया | |
| DEU | जर्मनी | 83.02 | 357.11 | 3693.20 | यूरोप | |
| FRA | फ्रांस | 67.02 | 640.68 | 2582.49 | यूरोप | 1789-07-14 |
| GBR | यूके | 66.44 | 242.50 | 2631.23 | यूरोप | |
| ITA | इटली | 60.36 | 301.34 | 1943.84 | यूरोप | |
| ARG | अर्जेंटीना | 44.94 | 2780.40 | 637.49 | S.America | 1816-07-09 |
| DZA | अल्जीरिया | 43.38 | 2381.74 | 167.56 | अफ्रीका | 1962-07-05 |
| CAN | कनाडा | 37.59 | 9984.67 | 1647.12 | N.America | 1867-07-01 |
| AUS | ऑस्ट्रेलिया | 25.47 | 7692.02 | 1408.68 | ओशिनिया | |
| KAZ | कजाखस्तान | 18.53 | 2724.90 | 159.41 | एशिया | 1991-12-16 |
आप देख सकते हैं कि कुछ डेटा गायब है। उदाहरण के लिए, रूस के लिए महाद्वीप निर्दिष्ट नहीं है क्योंकि यह यूरोप और एशिया दोनों में फैला हुआ है। कई स्वतंत्रता दिवस भी गायब हैं क्योंकि डेटा स्रोत उन्हें छोड़ देता है।
आप नेस्टेड शब्दकोश का उपयोग करके इस डेटा को पायथन में व्यवस्थित कर सकते हैं:
data = {
'CHN': {'COUNTRY': 'China', 'POP': 1_398.72, 'AREA': 9_596.96,
'GDP': 12_234.78, 'CONT': 'Asia'},
'IND': {'COUNTRY': 'India', 'POP': 1_351.16, 'AREA': 3_287.26,
'GDP': 2_575.67, 'CONT': 'Asia', 'IND_DAY': '1947-08-15'},
'USA': {'COUNTRY': 'US', 'POP': 329.74, 'AREA': 9_833.52,
'GDP': 19_485.39, 'CONT': 'N.America',
'IND_DAY': '1776-07-04'},
'IDN': {'COUNTRY': 'Indonesia', 'POP': 268.07, 'AREA': 1_910.93,
'GDP': 1_015.54, 'CONT': 'Asia', 'IND_DAY': '1945-08-17'},
'BRA': {'COUNTRY': 'Brazil', 'POP': 210.32, 'AREA': 8_515.77,
'GDP': 2_055.51, 'CONT': 'S.America', 'IND_DAY': '1822-09-07'},
'PAK': {'COUNTRY': 'Pakistan', 'POP': 205.71, 'AREA': 881.91,
'GDP': 302.14, 'CONT': 'Asia', 'IND_DAY': '1947-08-14'},
'NGA': {'COUNTRY': 'Nigeria', 'POP': 200.96, 'AREA': 923.77,
'GDP': 375.77, 'CONT': 'Africa', 'IND_DAY': '1960-10-01'},
'BGD': {'COUNTRY': 'Bangladesh', 'POP': 167.09, 'AREA': 147.57,
'GDP': 245.63, 'CONT': 'Asia', 'IND_DAY': '1971-03-26'},
'RUS': {'COUNTRY': 'Russia', 'POP': 146.79, 'AREA': 17_098.25,
'GDP': 1_530.75, 'IND_DAY': '1992-06-12'},
'MEX': {'COUNTRY': 'Mexico', 'POP': 126.58, 'AREA': 1_964.38,
'GDP': 1_158.23, 'CONT': 'N.America', 'IND_DAY': '1810-09-16'},
'JPN': {'COUNTRY': 'Japan', 'POP': 126.22, 'AREA': 377.97,
'GDP': 4_872.42, 'CONT': 'Asia'},
'DEU': {'COUNTRY': 'Germany', 'POP': 83.02, 'AREA': 357.11,
'GDP': 3_693.20, 'CONT': 'Europe'},
'FRA': {'COUNTRY': 'France', 'POP': 67.02, 'AREA': 640.68,
'GDP': 2_582.49, 'CONT': 'Europe', 'IND_DAY': '1789-07-14'},
'GBR': {'COUNTRY': 'UK', 'POP': 66.44, 'AREA': 242.50,
'GDP': 2_631.23, 'CONT': 'Europe'},
'ITA': {'COUNTRY': 'Italy', 'POP': 60.36, 'AREA': 301.34,
'GDP': 1_943.84, 'CONT': 'Europe'},
'ARG': {'COUNTRY': 'Argentina', 'POP': 44.94, 'AREA': 2_780.40,
'GDP': 637.49, 'CONT': 'S.America', 'IND_DAY': '1816-07-09'},
'DZA': {'COUNTRY': 'Algeria', 'POP': 43.38, 'AREA': 2_381.74,
'GDP': 167.56, 'CONT': 'Africa', 'IND_DAY': '1962-07-05'},
'CAN': {'COUNTRY': 'Canada', 'POP': 37.59, 'AREA': 9_984.67,
'GDP': 1_647.12, 'CONT': 'N.America', 'IND_DAY': '1867-07-01'},
'AUS': {'COUNTRY': 'Australia', 'POP': 25.47, 'AREA': 7_692.02,
'GDP': 1_408.68, 'CONT': 'Oceania'},
'KAZ': {'COUNTRY': 'Kazakhstan', 'POP': 18.53, 'AREA': 2_724.90,
'GDP': 159.41, 'CONT': 'Asia', 'IND_DAY': '1991-12-16'}
}
columns = ('COUNTRY', 'POP', 'AREA', 'GDP', 'CONT', 'IND_DAY')
तालिका की प्रत्येक पंक्ति को एक आंतरिक शब्दकोश के रूप में लिखा जाता है, जिसकी कुंजियाँ स्तंभ नाम होती हैं और मान संबंधित डेटा होते हैं। फिर इन शब्दकोशों को बाहरी data . में मानों के रूप में एकत्र किया जाता है शब्दकोश। data . के लिए संगत कुंजियां तीन-अक्षर वाले देश कोड हैं।
आप इस data का उपयोग कर सकते हैं पंडों का एक उदाहरण बनाने के लिए DataFrame . सबसे पहले, आपको पंडों को आयात करने की आवश्यकता है:
>>> import pandas as pd
अब जब आपने पंडों को आयात कर लिया है, तो आप DataFrame . का उपयोग कर सकते हैं कंस्ट्रक्टर और data एक DataFrame बनाने के लिए वस्तु।
data इस तरह से व्यवस्थित किया जाता है कि देश के कोड कॉलम के अनुरूप हों। आप DataFrame . की पंक्तियों और स्तंभों को उलट सकते हैं संपत्ति के साथ .T :
>>> df = pd.DataFrame(data=data).T
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.8 Asia NaN
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.4 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.2 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaN
DEU Germany 83.02 357.11 3693.2 Europe NaN
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.5 2631.23 Europe NaN
ITA Italy 60.36 301.34 1943.84 Europe NaN
ARG Argentina 44.94 2780.4 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaN
KAZ Kazakhstan 18.53 2724.9 159.41 Asia 1991-12-16
अब आपके पास आपका DataFrame है प्रत्येक देश के बारे में डेटा से भरी हुई वस्तु।
नोट: आप .transpose() . का उपयोग कर सकते हैं .T . के बजाय अपने डेटासेट की पंक्तियों और स्तंभों को उलटने के लिए। अगर आप .transpose() . का इस्तेमाल करते हैं , तो आप वैकल्पिक पैरामीटर सेट कर सकते हैं copy यह निर्दिष्ट करने के लिए कि क्या आप अंतर्निहित डेटा की प्रतिलिपि बनाना चाहते हैं। डिफ़ॉल्ट व्यवहार False है ।
3.6 से अधिक पुराने पायथन के संस्करण शब्दकोशों में चाबियों के क्रम की गारंटी नहीं देते हैं। यह सुनिश्चित करने के लिए कि पायथन और पंडों के पुराने संस्करणों के लिए स्तंभों का क्रम बना रहे, आप index=columns निर्दिष्ट कर सकते हैं :
>>> df = pd.DataFrame(data=data, index=columns).T
अब जब आपने अपना डेटा तैयार कर लिया है, तो आप फ़ाइलों के साथ काम करना शुरू करने के लिए तैयार हैं!
पंडों का उपयोग करना read_csv() और .to_csv() कार्य
अल्पविराम से अलग किए गए मान (CSV) फ़ाइल एक .csv के साथ एक सादा पाठ फ़ाइल है विस्तार जो सारणीबद्ध डेटा रखता है। यह बड़ी मात्रा में डेटा संग्रहीत करने के लिए सबसे लोकप्रिय फ़ाइल स्वरूपों में से एक है। CSV फ़ाइल की प्रत्येक पंक्ति एकल तालिका पंक्ति का प्रतिनिधित्व करती है। एक ही पंक्ति के मान डिफ़ॉल्ट रूप से अल्पविराम से अलग किए जाते हैं, लेकिन आप विभाजक को अर्धविराम, टैब, स्थान या किसी अन्य वर्ण में बदल सकते हैं।
एक CSV फ़ाइल लिखें
आप अपने पंडों को DataFrame सहेज सकते हैं एक CSV फ़ाइल के रूप में .to_csv() . के साथ :
>>> df.to_csv('data.csv')
इतना ही! आपने फ़ाइल बना ली है data.csv आपकी वर्तमान कार्यशील निर्देशिका में। आपकी CSV फ़ाइल कैसी दिखनी चाहिए, यह देखने के लिए आप नीचे दिए गए कोड ब्लॉक का विस्तार कर सकते हैं:
,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
CHN,China,1398.72,9596.96,12234.78,Asia,
IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15
USA,US,329.74,9833.52,19485.39,N.America,1776-07-04
IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17
BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07
PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14
NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01
BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26
RUS,Russia,146.79,17098.25,1530.75,,1992-06-12
MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16
JPN,Japan,126.22,377.97,4872.42,Asia,
DEU,Germany,83.02,357.11,3693.2,Europe,
FRA,France,67.02,640.68,2582.49,Europe,1789-07-14
GBR,UK,66.44,242.5,2631.23,Europe,
ITA,Italy,60.36,301.34,1943.84,Europe,
ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09
DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05
CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01
AUS,Australia,25.47,7692.02,1408.68,Oceania,
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16
इस टेक्स्ट फ़ाइल में अल्पविराम . से अलग किया गया डेटा है . पहले कॉलम में पंक्ति लेबल होते हैं। कुछ मामलों में, आप उन्हें अप्रासंगिक पाएंगे। यदि आप उन्हें नहीं रखना चाहते हैं, तो आप तर्क index=False पास कर सकते हैं करने के लिए .to_csv() ।
एक CSV फ़ाइल पढ़ें
एक बार जब आपका डेटा CSV फ़ाइल में सहेज लिया जाता है, तो आप समय-समय पर इसे लोड और उपयोग करना चाहेंगे। आप पंडों के साथ ऐसा कर सकते हैं read_csv() समारोह:
>>> df = pd.read_csv('data.csv', index_col=0)
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaN
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaN
DEU Germany 83.02 357.11 3693.20 Europe NaN
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaN
ITA Italy 60.36 301.34 1943.84 Europe NaN
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaN
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
इस मामले में, पंडों read_csv() फ़ंक्शन एक नया DataFrame देता है फ़ाइल से डेटा और लेबल के साथ data.csv , जिसे आपने पहले तर्क के साथ निर्दिष्ट किया था। यह स्ट्रिंग URL सहित कोई भी मान्य पथ हो सकती है।
पैरामीटर index_col CSV फ़ाइल के उस कॉलम को निर्दिष्ट करता है जिसमें पंक्ति लेबल होते हैं। आप इस पैरामीटर के लिए एक शून्य-आधारित स्तंभ अनुक्रमणिका असाइन करते हैं। आपको index_col . का मान निर्धारित करना चाहिए जब CSV फ़ाइल में डेटा के रूप में लोड करने से बचने के लिए पंक्ति लेबल होते हैं।
आप इस ट्यूटोरियल में बाद में CSV फ़ाइलों के साथ पांडा का उपयोग करने के बारे में अधिक जानेंगे। आप यह देखने के लिए पाइथन में सीएसवी फाइलों को पढ़ना और लिखना भी देख सकते हैं कि अंतर्निहित पायथन लाइब्रेरी सीएसवी के साथ सीएसवी फाइलों को कैसे संभालना है।
एक्सेल फ़ाइलें लिखने और पढ़ने के लिए पांडा का उपयोग करना
Microsoft Excel शायद सबसे व्यापक रूप से उपयोग किया जाने वाला स्प्रेडशीट सॉफ़्टवेयर है। जबकि पुराने संस्करण बाइनरी .xls . का उपयोग करते थे फ़ाइलें, Excel 2007 ने नया XML-आधारित .xlsx पेश किया फ़ाइल। आप CSV फ़ाइलों के समान, पंडों में Excel फ़ाइलें पढ़ और लिख सकते हैं। हालाँकि, आपको पहले निम्नलिखित पायथन पैकेज स्थापित करने होंगे:
- xlwt को लिखने के लिए
.xlsफ़ाइलें - openpyxl या XlsxWriter को लिखने के लिए
.xlsxफ़ाइलें - एक्सेल फ़ाइलें पढ़ने के लिए xlrd
आप उन्हें एक ही कमांड के साथ पाइप का उपयोग करके स्थापित कर सकते हैं:
$ pip install xlwt openpyxl xlsxwriter xlrd
आप कोंडा का भी उपयोग कर सकते हैं:
$ conda install xlwt openpyxl xlsxwriter xlrd
कृपया ध्यान दें कि आपको सभी इंस्टॉल करने की आवश्यकता नहीं है ये पैकेज। उदाहरण के लिए, आपको openpyxl और XlsxWriter दोनों की आवश्यकता नहीं है। यदि आप केवल .xls . के साथ काम करने जा रहे हैं फ़ाइलें, तो आपको उनमें से किसी की भी आवश्यकता नहीं है! हालांकि, अगर आप केवल .xlsx . के साथ काम करने का इरादा रखते हैं फ़ाइलें, तो आपको उनमें से कम से कम एक की आवश्यकता होगी, लेकिन नहीं xlwt . यह तय करने के लिए कुछ समय लें कि आपके प्रोजेक्ट के लिए कौन से पैकेज सही हैं।
एक्सेल फ़ाइल लिखें
एक बार जब आप उन पैकेजों को स्थापित कर लेते हैं, तो आप अपना DataFrame सहेज सकते हैं एक एक्सेल फ़ाइल में .to_excel() . के साथ :
>>> df.to_excel('data.xlsx')
तर्क 'data.xlsx' लक्ष्य फ़ाइल का प्रतिनिधित्व करता है और, वैकल्पिक रूप से, इसका पथ। ऊपर दिए गए स्टेटमेंट को data.xlsx . फ़ाइल बनानी चाहिए आपकी वर्तमान कार्यशील निर्देशिका में। वह फ़ाइल इस तरह दिखनी चाहिए:
फ़ाइल के पहले स्तंभ में पंक्तियों के लेबल होते हैं, जबकि अन्य स्तंभ डेटा संग्रहीत करते हैं।
एक्सेल फ़ाइल पढ़ें
आप एक्सेल फाइलों से read_excel() . के साथ डेटा लोड कर सकते हैं :
>>> df = pd.read_excel('data.xlsx', index_col=0)
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaN
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaN
DEU Germany 83.02 357.11 3693.20 Europe NaN
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaN
ITA Italy 60.36 301.34 1943.84 Europe NaN
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaN
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
read_excel() एक नया DataFrame लौटाता है जिसमें data.xlsx . के मान शामिल हैं . आप read_excel() . का भी उपयोग कर सकते हैं OpenDocument स्प्रैडशीट, या .ods . के साथ फ़ाइलें.
आप इस ट्यूटोरियल में बाद में एक्सेल फाइलों के साथ काम करने के बारे में और जानेंगे। आप पायथन में बड़ी एक्सेल फ़ाइलों को पढ़ने के लिए पंडों का उपयोग करना भी देख सकते हैं।
पंडों IO API को समझना
पंडस आईओ टूल्स वह एपीआई है जो आपको Series . की सामग्री को सहेजने की अनुमति देती है और DataFrame क्लिपबोर्ड, ऑब्जेक्ट, या विभिन्न प्रकार की फ़ाइलों के लिए ऑब्जेक्ट। यह क्लिपबोर्ड, ऑब्जेक्ट या फ़ाइलों से डेटा लोड करने में भी सक्षम बनाता है।
फ़ाइलें लिखें
Series और DataFrame ऑब्जेक्ट्स में ऐसी विधियाँ होती हैं जो क्लिपबोर्ड या फ़ाइलों में डेटा और लेबल लिखने में सक्षम बनाती हैं। उनका नाम .to_<file-type>() . पैटर्न के साथ रखा गया है , जहां <file-type> लक्ष्य फ़ाइल का प्रकार है।
आपने .to_csv() . के बारे में जान लिया है और .to_excel() , लेकिन अन्य भी हैं, जिनमें शामिल हैं:
.to_json().to_html().to_sql().to_pickle()
अभी और भी फ़ाइल प्रकार हैं जिन पर आप लिख सकते हैं, इसलिए यह सूची संपूर्ण नहीं है।
नोट: समान विधियों को खोजने के लिए, क्रमांकन, IO, और रूपांतरण से संबंधित Series के बारे में आधिकारिक दस्तावेज़ देखें। और DataFrame वस्तुओं।
इन विधियों में लक्ष्य फ़ाइल पथ निर्दिष्ट करने वाले पैरामीटर हैं जहां आपने डेटा और लेबल सहेजे हैं। यह कुछ मामलों में अनिवार्य है और दूसरों में वैकल्पिक है। यदि यह विकल्प उपलब्ध है और आप इसे छोड़ना चुनते हैं, तो विधियाँ DataFrame की सामग्री के साथ ऑब्जेक्ट (जैसे तार या पुनरावृत्त) लौटाती हैं उदाहरण।
वैकल्पिक पैरामीटर compression डेटा और लेबल के साथ फ़ाइल को संपीड़ित करने का तरीका तय करता है। आप इसके बारे में बाद में और जानेंगे। कुछ अन्य पैरामीटर हैं, लेकिन वे ज्यादातर एक या कई विधियों के लिए विशिष्ट हैं। आप यहां उनके बारे में विस्तार से नहीं जाएंगे।
फ़ाइलें पढ़ें
फ़ाइलों की सामग्री को पढ़ने के लिए पंडों के कार्यों को .read_<file-type>() पैटर्न का उपयोग करके नामित किया गया है , जहां <file-type> पढ़ने के लिए फ़ाइल के प्रकार को इंगित करता है। आप पंडों को पहले ही देख चुके हैं read_csv() और read_excel() कार्य। यहाँ कुछ अन्य हैं:
read_json()read_html()read_sql()read_pickle()
इन फ़ंक्शंस में एक पैरामीटर होता है जो लक्ष्य फ़ाइल पथ निर्दिष्ट करता है। यह कोई भी मान्य स्ट्रिंग हो सकती है जो पथ का प्रतिनिधित्व करती है, या तो स्थानीय मशीन पर या URL में। फ़ाइल प्रकार के आधार पर अन्य ऑब्जेक्ट भी स्वीकार्य हैं।
वैकल्पिक पैरामीटर compression संपीड़ित फ़ाइलों के लिए उपयोग करने के लिए डीकंप्रेसन के प्रकार को निर्धारित करता है। आप इसके बारे में बाद में इस ट्यूटोरियल में जानेंगे। अन्य पैरामीटर हैं, लेकिन वे एक या कई कार्यों के लिए विशिष्ट हैं। आप यहां उनके बारे में विस्तार से नहीं जाएंगे।
विभिन्न फ़ाइल प्रकारों के साथ कार्य करना
पांडस लाइब्रेरी आपके डेटा को फाइलों में सहेजने और फाइलों से डेटा लोड करने के लिए व्यापक संभावनाएं प्रदान करती है। इस अनुभाग में, आप CSV और Excel फ़ाइलों के साथ कार्य करने के बारे में अधिक जानेंगे। आप यह भी देखेंगे कि JSON, वेब पेज, डेटाबेस और पायथन अचार फ़ाइलों जैसी अन्य प्रकार की फ़ाइलों का उपयोग कैसे करें।
सीएसवी फ़ाइलें
आप पहले ही सीख चुके हैं कि CSV फ़ाइलों को कैसे पढ़ना और लिखना है। आइए अब विवरण में थोड़ा गहरा खोदें। जब आप .to_csv() . का उपयोग करते हैं अपना DataFrame save सहेजने के लिए , आप पैरामीटर के लिए एक तर्क प्रदान कर सकते हैं path_or_buf लक्ष्य फ़ाइल का पथ, नाम और विस्तार निर्दिष्ट करने के लिए।
path_or_buf पहला तर्क है .to_csv() लाऊंगा। यह कोई भी स्ट्रिंग हो सकती है जो एक वैध फ़ाइल पथ का प्रतिनिधित्व करती है जिसमें फ़ाइल का नाम और उसका एक्सटेंशन शामिल है। आपने इसे पिछले उदाहरण में देखा है। हालांकि, अगर आप path_or_buf . को छोड़ देते हैं , फिर .to_csv() कोई फाइल नहीं बनाएगा। इसके बजाय, यह संबंधित स्ट्रिंग लौटाएगा:
>>> df = pd.DataFrame(data=data).T
>>> s = df.to_csv()
>>> print(s)
,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
CHN,China,1398.72,9596.96,12234.78,Asia,
IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15
USA,US,329.74,9833.52,19485.39,N.America,1776-07-04
IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17
BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07
PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14
NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01
BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26
RUS,Russia,146.79,17098.25,1530.75,,1992-06-12
MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16
JPN,Japan,126.22,377.97,4872.42,Asia,
DEU,Germany,83.02,357.11,3693.2,Europe,
FRA,France,67.02,640.68,2582.49,Europe,1789-07-14
GBR,UK,66.44,242.5,2631.23,Europe,
ITA,Italy,60.36,301.34,1943.84,Europe,
ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09
DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05
CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01
AUS,Australia,25.47,7692.02,1408.68,Oceania,
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16
अब आपके पास स्ट्रिंग है s CSV फ़ाइल के बजाय। आपके पास कुछ अनुपलब्ध मान . भी हैं आपके DataFrame . में वस्तु। उदाहरण के लिए, रूस के लिए महाद्वीप और कई देशों (चीन, जापान, और इसी तरह) के लिए स्वतंत्रता दिवस उपलब्ध नहीं हैं। डेटा साइंस और मशीन लर्निंग में, आपको लापता मूल्यों को सावधानी से संभालना चाहिए। पांडा यहाँ उत्कृष्ट हैं! डिफ़ॉल्ट रूप से, पांडा लापता मानों को बदलने के लिए NaN मान का उपयोग करते हैं।
नोट: nan , जिसका अर्थ है "संख्या नहीं," पायथन में एक विशेष फ़्लोटिंग-पॉइंट मान है।
आप एक nan प्राप्त कर सकते हैं निम्नलिखित कार्यों में से किसी के साथ मूल्य:
float('nan')math.nannumpy.nan
df . में रूस से संबंधित महाद्वीप nan है :
>>> df.loc['RUS', 'CONT']
nan
यह उदाहरण .loc[] . का उपयोग करता है निर्दिष्ट पंक्ति और स्तंभ नामों के साथ डेटा प्राप्त करने के लिए।
जब आप अपना DataFrame सहेजते हैं CSV फ़ाइल में, खाली स्ट्रिंग्स ('' ) लापता डेटा का प्रतिनिधित्व करेगा। आप यह दोनों अपनी फ़ाइल data.csv . में देख सकते हैं और स्ट्रिंग में s . यदि आप इस व्यवहार को बदलना चाहते हैं, तो वैकल्पिक पैरामीटर का उपयोग करें na_rep :
>>> df.to_csv('new-data.csv', na_rep='(missing)')
यह कोड फ़ाइल बनाता है new-data.csv जहां लापता मान अब खाली तार नहीं हैं। यह फ़ाइल कैसी दिखनी चाहिए, यह देखने के लिए आप नीचे दिए गए कोड ब्लॉक का विस्तार कर सकते हैं:
,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
CHN,China,1398.72,9596.96,12234.78,Asia,(missing)
IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15
USA,US,329.74,9833.52,19485.39,N.America,1776-07-04
IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17
BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07
PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14
NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01
BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26
RUS,Russia,146.79,17098.25,1530.75,(missing),1992-06-12
MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16
JPN,Japan,126.22,377.97,4872.42,Asia,(missing)
DEU,Germany,83.02,357.11,3693.2,Europe,(missing)
FRA,France,67.02,640.68,2582.49,Europe,1789-07-14
GBR,UK,66.44,242.5,2631.23,Europe,(missing)
ITA,Italy,60.36,301.34,1943.84,Europe,(missing)
ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09
DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05
CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01
AUS,Australia,25.47,7692.02,1408.68,Oceania,(missing)
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16
Now, the string '(missing)' in the file corresponds to the nan values from df ।
When Pandas reads files, it considers the empty string ('' ) and a few others as missing values by default:
'nan''-nan''NA''N/A''NaN''null'
If you don’t want this behavior, then you can pass keep_default_na=False to the Pandas read_csv() function. To specify other labels for missing values, use the parameter na_values :
>>> pd.read_csv('new-data.csv', index_col=0, na_values='(missing)')
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaN
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaN
DEU Germany 83.02 357.11 3693.20 Europe NaN
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaN
ITA Italy 60.36 301.34 1943.84 Europe NaN
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaN
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
Here, you’ve marked the string '(missing)' as a new missing data label, and Pandas replaced it with nan when it read the file.
When you load data from a file, Pandas assigns the data types to the values of each column by default. You can check these types with .dtypes :
>>> df = pd.read_csv('data.csv', index_col=0)
>>> df.dtypes
COUNTRY object
POP float64
AREA float64
GDP float64
CONT object
IND_DAY object
dtype: object
The columns with strings and dates ('COUNTRY' , 'CONT' , and 'IND_DAY' ) have the data type object . Meanwhile, the numeric columns contain 64-bit floating-point numbers (float64 )।
You can use the parameter dtype to specify the desired data types and parse_dates to force use of datetimes:
>>> dtypes = {'POP': 'float32', 'AREA': 'float32', 'GDP': 'float32'}
>>> df = pd.read_csv('data.csv', index_col=0, dtype=dtypes,
... parse_dates=['IND_DAY'])
>>> df.dtypes
COUNTRY object
POP float32
AREA float32
GDP float32
CONT object
IND_DAY datetime64[ns]
dtype: object
>>> df['IND_DAY']
CHN NaT
IND 1947-08-15
USA 1776-07-04
IDN 1945-08-17
BRA 1822-09-07
PAK 1947-08-14
NGA 1960-10-01
BGD 1971-03-26
RUS 1992-06-12
MEX 1810-09-16
JPN NaT
DEU NaT
FRA 1789-07-14
GBR NaT
ITA NaT
ARG 1816-07-09
DZA 1962-07-05
CAN 1867-07-01
AUS NaT
KAZ 1991-12-16
Name: IND_DAY, dtype: datetime64[ns]
Now, you have 32-bit floating-point numbers (float32 ) as specified with dtype . These differ slightly from the original 64-bit numbers because of smaller precision . The values in the last column are considered as dates and have the data type datetime64 . That’s why the NaN values in this column are replaced with NaT ।
Now that you have real dates, you can save them in the format you like:
>>>>>> df = pd.read_csv('data.csv', index_col=0, parse_dates=['IND_DAY'])
>>> df.to_csv('formatted-data.csv', date_format='%B %d, %Y')
Here, you’ve specified the parameter date_format to be '%B %d, %Y' . You can expand the code block below to see the resulting file:
,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
CHN,China,1398.72,9596.96,12234.78,Asia,
IND,India,1351.16,3287.26,2575.67,Asia,"August 15, 1947"
USA,US,329.74,9833.52,19485.39,N.America,"July 04, 1776"
IDN,Indonesia,268.07,1910.93,1015.54,Asia,"August 17, 1945"
BRA,Brazil,210.32,8515.77,2055.51,S.America,"September 07, 1822"
PAK,Pakistan,205.71,881.91,302.14,Asia,"August 14, 1947"
NGA,Nigeria,200.96,923.77,375.77,Africa,"October 01, 1960"
BGD,Bangladesh,167.09,147.57,245.63,Asia,"March 26, 1971"
RUS,Russia,146.79,17098.25,1530.75,,"June 12, 1992"
MEX,Mexico,126.58,1964.38,1158.23,N.America,"September 16, 1810"
JPN,Japan,126.22,377.97,4872.42,Asia,
DEU,Germany,83.02,357.11,3693.2,Europe,
FRA,France,67.02,640.68,2582.49,Europe,"July 14, 1789"
GBR,UK,66.44,242.5,2631.23,Europe,
ITA,Italy,60.36,301.34,1943.84,Europe,
ARG,Argentina,44.94,2780.4,637.49,S.America,"July 09, 1816"
DZA,Algeria,43.38,2381.74,167.56,Africa,"July 05, 1962"
CAN,Canada,37.59,9984.67,1647.12,N.America,"July 01, 1867"
AUS,Australia,25.47,7692.02,1408.68,Oceania,
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,"December 16, 1991"
The format of the dates is different now. The format '%B %d, %Y' means the date will first display the full name of the month, then the day followed by a comma, and finally the full year.
There are several other optional parameters that you can use with .to_csv() :
sepdenotes a values separator.decimalindicates a decimal separator.encodingsets the file encoding.headerspecifies whether you want to write column labels in the file.
Here’s how you would pass arguments for sep and header :
>>> s = df.to_csv(sep=';', header=False)
>>> print(s)
CHN;China;1398.72;9596.96;12234.78;Asia;
IND;India;1351.16;3287.26;2575.67;Asia;1947-08-15
USA;US;329.74;9833.52;19485.39;N.America;1776-07-04
IDN;Indonesia;268.07;1910.93;1015.54;Asia;1945-08-17
BRA;Brazil;210.32;8515.77;2055.51;S.America;1822-09-07
PAK;Pakistan;205.71;881.91;302.14;Asia;1947-08-14
NGA;Nigeria;200.96;923.77;375.77;Africa;1960-10-01
BGD;Bangladesh;167.09;147.57;245.63;Asia;1971-03-26
RUS;Russia;146.79;17098.25;1530.75;;1992-06-12
MEX;Mexico;126.58;1964.38;1158.23;N.America;1810-09-16
JPN;Japan;126.22;377.97;4872.42;Asia;
DEU;Germany;83.02;357.11;3693.2;Europe;
FRA;France;67.02;640.68;2582.49;Europe;1789-07-14
GBR;UK;66.44;242.5;2631.23;Europe;
ITA;Italy;60.36;301.34;1943.84;Europe;
ARG;Argentina;44.94;2780.4;637.49;S.America;1816-07-09
DZA;Algeria;43.38;2381.74;167.56;Africa;1962-07-05
CAN;Canada;37.59;9984.67;1647.12;N.America;1867-07-01
AUS;Australia;25.47;7692.02;1408.68;Oceania;
KAZ;Kazakhstan;18.53;2724.9;159.41;Asia;1991-12-16
The data is separated with a semicolon (';' ) because you’ve specified sep=';' . Also, since you passed header=False , you see your data without the header row of column names.
The Pandas read_csv() function has many additional options for managing missing data, working with dates and times, quoting, encoding, handling errors, and more. For instance, if you have a file with one data column and want to get a Series object instead of a DataFrame , then you can pass squeeze=True to read_csv() . You’ll learn later on about data compression and decompression, as well as how to skip rows and columns.
JSON Files
JSON stands for JavaScript object notation. JSON files are plaintext files used for data interchange, and humans can read them easily. They follow the ISO/IEC 21778:2017 and ECMA-404 standards and use the .json extension. Python and Pandas work well with JSON files, as Python’s json library offers built-in support for them.
You can save the data from your DataFrame to a JSON file with .to_json() . Start by creating a DataFrame object again. Use the dictionary data that holds the data about countries and then apply .to_json() :
>>> df = pd.DataFrame(data=data).T
>>> df.to_json('data-columns.json')
This code produces the file data-columns.json . You can expand the code block below to see how this file should look:
{"COUNTRY":{"CHN":"China","IND":"India","USA":"US","IDN":"Indonesia","BRA":"Brazil","PAK":"Pakistan","NGA":"Nigeria","BGD":"Bangladesh","RUS":"Russia","MEX":"Mexico","JPN":"Japan","DEU":"Germany","FRA":"France","GBR":"UK","ITA":"Italy","ARG":"Argentina","DZA":"Algeria","CAN":"Canada","AUS":"Australia","KAZ":"Kazakhstan"},"POP":{"CHN":1398.72,"IND":1351.16,"USA":329.74,"IDN":268.07,"BRA":210.32,"PAK":205.71,"NGA":200.96,"BGD":167.09,"RUS":146.79,"MEX":126.58,"JPN":126.22,"DEU":83.02,"FRA":67.02,"GBR":66.44,"ITA":60.36,"ARG":44.94,"DZA":43.38,"CAN":37.59,"AUS":25.47,"KAZ":18.53},"AREA":{"CHN":9596.96,"IND":3287.26,"USA":9833.52,"IDN":1910.93,"BRA":8515.77,"PAK":881.91,"NGA":923.77,"BGD":147.57,"RUS":17098.25,"MEX":1964.38,"JPN":377.97,"DEU":357.11,"FRA":640.68,"GBR":242.5,"ITA":301.34,"ARG":2780.4,"DZA":2381.74,"CAN":9984.67,"AUS":7692.02,"KAZ":2724.9},"GDP":{"CHN":12234.78,"IND":2575.67,"USA":19485.39,"IDN":1015.54,"BRA":2055.51,"PAK":302.14,"NGA":375.77,"BGD":245.63,"RUS":1530.75,"MEX":1158.23,"JPN":4872.42,"DEU":3693.2,"FRA":2582.49,"GBR":2631.23,"ITA":1943.84,"ARG":637.49,"DZA":167.56,"CAN":1647.12,"AUS":1408.68,"KAZ":159.41},"CONT":{"CHN":"Asia","IND":"Asia","USA":"N.America","IDN":"Asia","BRA":"S.America","PAK":"Asia","NGA":"Africa","BGD":"Asia","RUS":null,"MEX":"N.America","JPN":"Asia","DEU":"Europe","FRA":"Europe","GBR":"Europe","ITA":"Europe","ARG":"S.America","DZA":"Africa","CAN":"N.America","AUS":"Oceania","KAZ":"Asia"},"IND_DAY":{"CHN":null,"IND":"1947-08-15","USA":"1776-07-04","IDN":"1945-08-17","BRA":"1822-09-07","PAK":"1947-08-14","NGA":"1960-10-01","BGD":"1971-03-26","RUS":"1992-06-12","MEX":"1810-09-16","JPN":null,"DEU":null,"FRA":"1789-07-14","GBR":null,"ITA":null,"ARG":"1816-07-09","DZA":"1962-07-05","CAN":"1867-07-01","AUS":null,"KAZ":"1991-12-16"}}
data-columns.json has one large dictionary with the column labels as keys and the corresponding inner dictionaries as values.
You can get a different file structure if you pass an argument for the optional parameter orient :
>>> df.to_json('data-index.json', orient='index')
The orient parameter defaults to 'columns' . Here, you’ve set it to index ।
You should get a new file data-index.json . You can expand the code block below to see the changes:
{"CHN":{"COUNTRY":"China","POP":1398.72,"AREA":9596.96,"GDP":12234.78,"CONT":"Asia","IND_DAY":null},"IND":{"COUNTRY":"India","POP":1351.16,"AREA":3287.26,"GDP":2575.67,"CONT":"Asia","IND_DAY":"1947-08-15"},"USA":{"COUNTRY":"US","POP":329.74,"AREA":9833.52,"GDP":19485.39,"CONT":"N.America","IND_DAY":"1776-07-04"},"IDN":{"COUNTRY":"Indonesia","POP":268.07,"AREA":1910.93,"GDP":1015.54,"CONT":"Asia","IND_DAY":"1945-08-17"},"BRA":{"COUNTRY":"Brazil","POP":210.32,"AREA":8515.77,"GDP":2055.51,"CONT":"S.America","IND_DAY":"1822-09-07"},"PAK":{"COUNTRY":"Pakistan","POP":205.71,"AREA":881.91,"GDP":302.14,"CONT":"Asia","IND_DAY":"1947-08-14"},"NGA":{"COUNTRY":"Nigeria","POP":200.96,"AREA":923.77,"GDP":375.77,"CONT":"Africa","IND_DAY":"1960-10-01"},"BGD":{"COUNTRY":"Bangladesh","POP":167.09,"AREA":147.57,"GDP":245.63,"CONT":"Asia","IND_DAY":"1971-03-26"},"RUS":{"COUNTRY":"Russia","POP":146.79,"AREA":17098.25,"GDP":1530.75,"CONT":null,"IND_DAY":"1992-06-12"},"MEX":{"COUNTRY":"Mexico","POP":126.58,"AREA":1964.38,"GDP":1158.23,"CONT":"N.America","IND_DAY":"1810-09-16"},"JPN":{"COUNTRY":"Japan","POP":126.22,"AREA":377.97,"GDP":4872.42,"CONT":"Asia","IND_DAY":null},"DEU":{"COUNTRY":"Germany","POP":83.02,"AREA":357.11,"GDP":3693.2,"CONT":"Europe","IND_DAY":null},"FRA":{"COUNTRY":"France","POP":67.02,"AREA":640.68,"GDP":2582.49,"CONT":"Europe","IND_DAY":"1789-07-14"},"GBR":{"COUNTRY":"UK","POP":66.44,"AREA":242.5,"GDP":2631.23,"CONT":"Europe","IND_DAY":null},"ITA":{"COUNTRY":"Italy","POP":60.36,"AREA":301.34,"GDP":1943.84,"CONT":"Europe","IND_DAY":null},"ARG":{"COUNTRY":"Argentina","POP":44.94,"AREA":2780.4,"GDP":637.49,"CONT":"S.America","IND_DAY":"1816-07-09"},"DZA":{"COUNTRY":"Algeria","POP":43.38,"AREA":2381.74,"GDP":167.56,"CONT":"Africa","IND_DAY":"1962-07-05"},"CAN":{"COUNTRY":"Canada","POP":37.59,"AREA":9984.67,"GDP":1647.12,"CONT":"N.America","IND_DAY":"1867-07-01"},"AUS":{"COUNTRY":"Australia","POP":25.47,"AREA":7692.02,"GDP":1408.68,"CONT":"Oceania","IND_DAY":null},"KAZ":{"COUNTRY":"Kazakhstan","POP":18.53,"AREA":2724.9,"GDP":159.41,"CONT":"Asia","IND_DAY":"1991-12-16"}}
data-index.json also has one large dictionary, but this time the row labels are the keys, and the inner dictionaries are the values.
There are few more options for orient . One of them is 'records' :
>>> df.to_json('data-records.json', orient='records')
This code should yield the file data-records.json . You can expand the code block below to see the content:
[{"COUNTRY":"China","POP":1398.72,"AREA":9596.96,"GDP":12234.78,"CONT":"Asia","IND_DAY":null},{"COUNTRY":"India","POP":1351.16,"AREA":3287.26,"GDP":2575.67,"CONT":"Asia","IND_DAY":"1947-08-15"},{"COUNTRY":"US","POP":329.74,"AREA":9833.52,"GDP":19485.39,"CONT":"N.America","IND_DAY":"1776-07-04"},{"COUNTRY":"Indonesia","POP":268.07,"AREA":1910.93,"GDP":1015.54,"CONT":"Asia","IND_DAY":"1945-08-17"},{"COUNTRY":"Brazil","POP":210.32,"AREA":8515.77,"GDP":2055.51,"CONT":"S.America","IND_DAY":"1822-09-07"},{"COUNTRY":"Pakistan","POP":205.71,"AREA":881.91,"GDP":302.14,"CONT":"Asia","IND_DAY":"1947-08-14"},{"COUNTRY":"Nigeria","POP":200.96,"AREA":923.77,"GDP":375.77,"CONT":"Africa","IND_DAY":"1960-10-01"},{"COUNTRY":"Bangladesh","POP":167.09,"AREA":147.57,"GDP":245.63,"CONT":"Asia","IND_DAY":"1971-03-26"},{"COUNTRY":"Russia","POP":146.79,"AREA":17098.25,"GDP":1530.75,"CONT":null,"IND_DAY":"1992-06-12"},{"COUNTRY":"Mexico","POP":126.58,"AREA":1964.38,"GDP":1158.23,"CONT":"N.America","IND_DAY":"1810-09-16"},{"COUNTRY":"Japan","POP":126.22,"AREA":377.97,"GDP":4872.42,"CONT":"Asia","IND_DAY":null},{"COUNTRY":"Germany","POP":83.02,"AREA":357.11,"GDP":3693.2,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"France","POP":67.02,"AREA":640.68,"GDP":2582.49,"CONT":"Europe","IND_DAY":"1789-07-14"},{"COUNTRY":"UK","POP":66.44,"AREA":242.5,"GDP":2631.23,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"Italy","POP":60.36,"AREA":301.34,"GDP":1943.84,"CONT":"Europe","IND_DAY":null},{"COUNTRY":"Argentina","POP":44.94,"AREA":2780.4,"GDP":637.49,"CONT":"S.America","IND_DAY":"1816-07-09"},{"COUNTRY":"Algeria","POP":43.38,"AREA":2381.74,"GDP":167.56,"CONT":"Africa","IND_DAY":"1962-07-05"},{"COUNTRY":"Canada","POP":37.59,"AREA":9984.67,"GDP":1647.12,"CONT":"N.America","IND_DAY":"1867-07-01"},{"COUNTRY":"Australia","POP":25.47,"AREA":7692.02,"GDP":1408.68,"CONT":"Oceania","IND_DAY":null},{"COUNTRY":"Kazakhstan","POP":18.53,"AREA":2724.9,"GDP":159.41,"CONT":"Asia","IND_DAY":"1991-12-16"}]
data-records.json holds a list with one dictionary for each row. The row labels are not written.
You can get another interesting file structure with orient='split' :
>>> df.to_json('data-split.json', orient='split')
The resulting file is data-split.json . You can expand the code block below to see how this file should look:
{"columns":["COUNTRY","POP","AREA","GDP","CONT","IND_DAY"],"index":["CHN","IND","USA","IDN","BRA","PAK","NGA","BGD","RUS","MEX","JPN","DEU","FRA","GBR","ITA","ARG","DZA","CAN","AUS","KAZ"],"data":[["China",1398.72,9596.96,12234.78,"Asia",null],["India",1351.16,3287.26,2575.67,"Asia","1947-08-15"],["US",329.74,9833.52,19485.39,"N.America","1776-07-04"],["Indonesia",268.07,1910.93,1015.54,"Asia","1945-08-17"],["Brazil",210.32,8515.77,2055.51,"S.America","1822-09-07"],["Pakistan",205.71,881.91,302.14,"Asia","1947-08-14"],["Nigeria",200.96,923.77,375.77,"Africa","1960-10-01"],["Bangladesh",167.09,147.57,245.63,"Asia","1971-03-26"],["Russia",146.79,17098.25,1530.75,null,"1992-06-12"],["Mexico",126.58,1964.38,1158.23,"N.America","1810-09-16"],["Japan",126.22,377.97,4872.42,"Asia",null],["Germany",83.02,357.11,3693.2,"Europe",null],["France",67.02,640.68,2582.49,"Europe","1789-07-14"],["UK",66.44,242.5,2631.23,"Europe",null],["Italy",60.36,301.34,1943.84,"Europe",null],["Argentina",44.94,2780.4,637.49,"S.America","1816-07-09"],["Algeria",43.38,2381.74,167.56,"Africa","1962-07-05"],["Canada",37.59,9984.67,1647.12,"N.America","1867-07-01"],["Australia",25.47,7692.02,1408.68,"Oceania",null],["Kazakhstan",18.53,2724.9,159.41,"Asia","1991-12-16"]]}
data-split.json contains one dictionary that holds the following lists:
- The names of the columns
- The labels of the rows
- The inner lists (two-dimensional sequence) that hold data values
If you don’t provide the value for the optional parameter path_or_buf that defines the file path, then .to_json() will return a JSON string instead of writing the results to a file. This behavior is consistent with .to_csv() ।
There are other optional parameters you can use. For instance, you can set index=False to forgo saving row labels. You can manipulate precision with double_precision , and dates with date_format and date_unit . These last two parameters are particularly important when you have time series among your data:
>>> df = pd.DataFrame(data=data).T
>>> df['IND_DAY'] = pd.to_datetime(df['IND_DAY'])
>>> df.dtypes
COUNTRY object
POP object
AREA object
GDP object
CONT object
IND_DAY datetime64[ns]
dtype: object
>>> df.to_json('data-time.json')
In this example, you’ve created the DataFrame from the dictionary data and used to_datetime() to convert the values in the last column to datetime64 . You can expand the code block below to see the resulting file:
{"COUNTRY":{"CHN":"China","IND":"India","USA":"US","IDN":"Indonesia","BRA":"Brazil","PAK":"Pakistan","NGA":"Nigeria","BGD":"Bangladesh","RUS":"Russia","MEX":"Mexico","JPN":"Japan","DEU":"Germany","FRA":"France","GBR":"UK","ITA":"Italy","ARG":"Argentina","DZA":"Algeria","CAN":"Canada","AUS":"Australia","KAZ":"Kazakhstan"},"POP":{"CHN":1398.72,"IND":1351.16,"USA":329.74,"IDN":268.07,"BRA":210.32,"PAK":205.71,"NGA":200.96,"BGD":167.09,"RUS":146.79,"MEX":126.58,"JPN":126.22,"DEU":83.02,"FRA":67.02,"GBR":66.44,"ITA":60.36,"ARG":44.94,"DZA":43.38,"CAN":37.59,"AUS":25.47,"KAZ":18.53},"AREA":{"CHN":9596.96,"IND":3287.26,"USA":9833.52,"IDN":1910.93,"BRA":8515.77,"PAK":881.91,"NGA":923.77,"BGD":147.57,"RUS":17098.25,"MEX":1964.38,"JPN":377.97,"DEU":357.11,"FRA":640.68,"GBR":242.5,"ITA":301.34,"ARG":2780.4,"DZA":2381.74,"CAN":9984.67,"AUS":7692.02,"KAZ":2724.9},"GDP":{"CHN":12234.78,"IND":2575.67,"USA":19485.39,"IDN":1015.54,"BRA":2055.51,"PAK":302.14,"NGA":375.77,"BGD":245.63,"RUS":1530.75,"MEX":1158.23,"JPN":4872.42,"DEU":3693.2,"FRA":2582.49,"GBR":2631.23,"ITA":1943.84,"ARG":637.49,"DZA":167.56,"CAN":1647.12,"AUS":1408.68,"KAZ":159.41},"CONT":{"CHN":"Asia","IND":"Asia","USA":"N.America","IDN":"Asia","BRA":"S.America","PAK":"Asia","NGA":"Africa","BGD":"Asia","RUS":null,"MEX":"N.America","JPN":"Asia","DEU":"Europe","FRA":"Europe","GBR":"Europe","ITA":"Europe","ARG":"S.America","DZA":"Africa","CAN":"N.America","AUS":"Oceania","KAZ":"Asia"},"IND_DAY":{"CHN":null,"IND":-706320000000,"USA":-6106060800000,"IDN":-769219200000,"BRA":-4648924800000,"PAK":-706406400000,"NGA":-291945600000,"BGD":38793600000,"RUS":708307200000,"MEX":-5026838400000,"JPN":null,"DEU":null,"FRA":-5694969600000,"GBR":null,"ITA":null,"ARG":-4843411200000,"DZA":-236476800000,"CAN":-3234729600000,"AUS":null,"KAZ":692841600000}}
In this file, you have large integers instead of dates for the independence days. That’s because the default value of the optional parameter date_format is 'epoch' whenever orient isn’t 'table' . This default behavior expresses dates as an epoch in milliseconds relative to midnight on January 1, 1970.
However, if you pass date_format='iso' , then you’ll get the dates in the ISO 8601 format. In addition, date_unit decides the units of time:
>>> df = pd.DataFrame(data=data).T
>>> df['IND_DAY'] = pd.to_datetime(df['IND_DAY'])
>>> df.to_json('new-data-time.json', date_format='iso', date_unit='s')
This code produces the following JSON file:
{"COUNTRY":{"CHN":"China","IND":"India","USA":"US","IDN":"Indonesia","BRA":"Brazil","PAK":"Pakistan","NGA":"Nigeria","BGD":"Bangladesh","RUS":"Russia","MEX":"Mexico","JPN":"Japan","DEU":"Germany","FRA":"France","GBR":"UK","ITA":"Italy","ARG":"Argentina","DZA":"Algeria","CAN":"Canada","AUS":"Australia","KAZ":"Kazakhstan"},"POP":{"CHN":1398.72,"IND":1351.16,"USA":329.74,"IDN":268.07,"BRA":210.32,"PAK":205.71,"NGA":200.96,"BGD":167.09,"RUS":146.79,"MEX":126.58,"JPN":126.22,"DEU":83.02,"FRA":67.02,"GBR":66.44,"ITA":60.36,"ARG":44.94,"DZA":43.38,"CAN":37.59,"AUS":25.47,"KAZ":18.53},"AREA":{"CHN":9596.96,"IND":3287.26,"USA":9833.52,"IDN":1910.93,"BRA":8515.77,"PAK":881.91,"NGA":923.77,"BGD":147.57,"RUS":17098.25,"MEX":1964.38,"JPN":377.97,"DEU":357.11,"FRA":640.68,"GBR":242.5,"ITA":301.34,"ARG":2780.4,"DZA":2381.74,"CAN":9984.67,"AUS":7692.02,"KAZ":2724.9},"GDP":{"CHN":12234.78,"IND":2575.67,"USA":19485.39,"IDN":1015.54,"BRA":2055.51,"PAK":302.14,"NGA":375.77,"BGD":245.63,"RUS":1530.75,"MEX":1158.23,"JPN":4872.42,"DEU":3693.2,"FRA":2582.49,"GBR":2631.23,"ITA":1943.84,"ARG":637.49,"DZA":167.56,"CAN":1647.12,"AUS":1408.68,"KAZ":159.41},"CONT":{"CHN":"Asia","IND":"Asia","USA":"N.America","IDN":"Asia","BRA":"S.America","PAK":"Asia","NGA":"Africa","BGD":"Asia","RUS":null,"MEX":"N.America","JPN":"Asia","DEU":"Europe","FRA":"Europe","GBR":"Europe","ITA":"Europe","ARG":"S.America","DZA":"Africa","CAN":"N.America","AUS":"Oceania","KAZ":"Asia"},"IND_DAY":{"CHN":null,"IND":"1947-08-15T00:00:00Z","USA":"1776-07-04T00:00:00Z","IDN":"1945-08-17T00:00:00Z","BRA":"1822-09-07T00:00:00Z","PAK":"1947-08-14T00:00:00Z","NGA":"1960-10-01T00:00:00Z","BGD":"1971-03-26T00:00:00Z","RUS":"1992-06-12T00:00:00Z","MEX":"1810-09-16T00:00:00Z","JPN":null,"DEU":null,"FRA":"1789-07-14T00:00:00Z","GBR":null,"ITA":null,"ARG":"1816-07-09T00:00:00Z","DZA":"1962-07-05T00:00:00Z","CAN":"1867-07-01T00:00:00Z","AUS":null,"KAZ":"1991-12-16T00:00:00Z"}}
The dates in the resulting file are in the ISO 8601 format.
You can load the data from a JSON file with read_json() :
>>> df = pd.read_json('data-index.json', orient='index',
... convert_dates=['IND_DAY'])
The parameter convert_dates has a similar purpose as parse_dates when you use it to read CSV files. The optional parameter orient is very important because it specifies how Pandas understands the structure of the file.
There are other optional parameters you can use as well:
- Set the encoding with
encoding। - Manipulate dates with
convert_datesandkeep_default_dates। - Impact precision with
dtypeandprecise_float। - Decode numeric data directly to NumPy arrays with
numpy=True।
Note that you might lose the order of rows and columns when using the JSON format to store your data.
HTML Files
An HTML is a plaintext file that uses hypertext markup language to help browsers render web pages. The extensions for HTML files are .html and .htm . You’ll need to install an HTML parser library like lxml or html5lib to be able to work with HTML files:
$pip install lxml html5lib
You can also use Conda to install the same packages:
$ conda install lxml html5lib
Once you have these libraries, you can save the contents of your DataFrame as an HTML file with .to_html() :
df = pd.DataFrame(data=data).T
df.to_html('data.html')
This code generates a file data.html . You can expand the code block below to see how this file should look:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>COUNTRY</th>
<th>POP</th>
<th>AREA</th>
<th>GDP</th>
<th>CONT</th>
<th>IND_DAY</th>
</tr>
</thead>
<tbody>
<tr>
<th>CHN</th>
<td>China</td>
<td>1398.72</td>
<td>9596.96</td>
<td>12234.8</td>
<td>Asia</td>
<td>NaN</td>
</tr>
<tr>
<th>IND</th>
<td>India</td>
<td>1351.16</td>
<td>3287.26</td>
<td>2575.67</td>
<td>Asia</td>
<td>1947-08-15</td>
</tr>
<tr>
<th>USA</th>
<td>US</td>
<td>329.74</td>
<td>9833.52</td>
<td>19485.4</td>
<td>N.America</td>
<td>1776-07-04</td>
</tr>
<tr>
<th>IDN</th>
<td>Indonesia</td>
<td>268.07</td>
<td>1910.93</td>
<td>1015.54</td>
<td>Asia</td>
<td>1945-08-17</td>
</tr>
<tr>
<th>BRA</th>
<td>Brazil</td>
<td>210.32</td>
<td>8515.77</td>
<td>2055.51</td>
<td>S.America</td>
<td>1822-09-07</td>
</tr>
<tr>
<th>PAK</th>
<td>Pakistan</td>
<td>205.71</td>
<td>881.91</td>
<td>302.14</td>
<td>Asia</td>
<td>1947-08-14</td>
</tr>
<tr>
<th>NGA</th>
<td>Nigeria</td>
<td>200.96</td>
<td>923.77</td>
<td>375.77</td>
<td>Africa</td>
<td>1960-10-01</td>
</tr>
<tr>
<th>BGD</th>
<td>Bangladesh</td>
<td>167.09</td>
<td>147.57</td>
<td>245.63</td>
<td>Asia</td>
<td>1971-03-26</td>
</tr>
<tr>
<th>RUS</th>
<td>Russia</td>
<td>146.79</td>
<td>17098.2</td>
<td>1530.75</td>
<td>NaN</td>
<td>1992-06-12</td>
</tr>
<tr>
<th>MEX</th>
<td>Mexico</td>
<td>126.58</td>
<td>1964.38</td>
<td>1158.23</td>
<td>N.America</td>
<td>1810-09-16</td>
</tr>
<tr>
<th>JPN</th>
<td>Japan</td>
<td>126.22</td>
<td>377.97</td>
<td>4872.42</td>
<td>Asia</td>
<td>NaN</td>
</tr>
<tr>
<th>DEU</th>
<td>Germany</td>
<td>83.02</td>
<td>357.11</td>
<td>3693.2</td>
<td>Europe</td>
<td>NaN</td>
</tr>
<tr>
<th>FRA</th>
<td>France</td>
<td>67.02</td>
<td>640.68</td>
<td>2582.49</td>
<td>Europe</td>
<td>1789-07-14</td>
</tr>
<tr>
<th>GBR</th>
<td>UK</td>
<td>66.44</td>
<td>242.5</td>
<td>2631.23</td>
<td>Europe</td>
<td>NaN</td>
</tr>
<tr>
<th>ITA</th>
<td>Italy</td>
<td>60.36</td>
<td>301.34</td>
<td>1943.84</td>
<td>Europe</td>
<td>NaN</td>
</tr>
<tr>
<th>ARG</th>
<td>Argentina</td>
<td>44.94</td>
<td>2780.4</td>
<td>637.49</td>
<td>S.America</td>
<td>1816-07-09</td>
</tr>
<tr>
<th>DZA</th>
<td>Algeria</td>
<td>43.38</td>
<td>2381.74</td>
<td>167.56</td>
<td>Africa</td>
<td>1962-07-05</td>
</tr>
<tr>
<th>CAN</th>
<td>Canada</td>
<td>37.59</td>
<td>9984.67</td>
<td>1647.12</td>
<td>N.America</td>
<td>1867-07-01</td>
</tr>
<tr>
<th>AUS</th>
<td>Australia</td>
<td>25.47</td>
<td>7692.02</td>
<td>1408.68</td>
<td>Oceania</td>
<td>NaN</td>
</tr>
<tr>
<th>KAZ</th>
<td>Kazakhstan</td>
<td>18.53</td>
<td>2724.9</td>
<td>159.41</td>
<td>Asia</td>
<td>1991-12-16</td>
</tr>
</tbody>
</table>
This file shows the DataFrame contents nicely. However, notice that you haven’t obtained an entire web page. You’ve just output the data that corresponds to df in the HTML format.
.to_html() won’t create a file if you don’t provide the optional parameter buf , which denotes the buffer to write to. If you leave this parameter out, then your code will return a string as it did with .to_csv() and .to_json() ।
Here are some other optional parameters:
headerdetermines whether to save the column names.indexdetermines whether to save the row labels.classesassigns cascading style sheet (CSS) classes.render_linksspecifies whether to convert URLs to HTML links.table_idassigns the CSSidto thetabletag.escapedecides whether to convert the characters<,>, and&to HTML-safe strings.
You use parameters like these to specify different aspects of the resulting files or strings.
You can create a DataFrame object from a suitable HTML file using read_html() , which will return a DataFrame instance or a list of them:
>>> df = pd.read_html('data.html', index_col=0, parse_dates=['IND_DAY'])
This is very similar to what you did when reading CSV files. You also have parameters that help you work with dates, missing values, precision, encoding, HTML parsers, and more.
Excel Files
You’ve already learned how to read and write Excel files with Pandas. However, there are a few more options worth considering. For one, when you use .to_excel() , you can specify the name of the target worksheet with the optional parameter sheet_name :
>>> df = pd.DataFrame(data=data).T
>>> df.to_excel('data.xlsx', sheet_name='COUNTRIES')
Here, you create a file data.xlsx with a worksheet called COUNTRIES that stores the data. The string 'data.xlsx' is the argument for the parameter excel_writer that defines the name of the Excel file or its path.
The optional parameters startrow and startcol both default to 0 and indicate the upper left-most cell where the data should start being written:
>>> df.to_excel('data-shifted.xlsx', sheet_name='COUNTRIES',
... startrow=2, startcol=4)
Here, you specify that the table should start in the third row and the fifth column. You also used zero-based indexing, so the third row is denoted by 2 and the fifth column by 4 ।
Now the resulting worksheet looks like this:
As you can see, the table starts in the third row 2 and the fifth column E ।
.read_excel() also has the optional parameter sheet_name that specifies which worksheets to read when loading data. It can take on one of the following values:
- The zero-based index of the worksheet
- The name of the worksheet
- The list of indices or names to read multiple sheets
- The value
Noneto read all sheets
Here’s how you would use this parameter in your code:
>>>>>> df = pd.read_excel('data.xlsx', sheet_name=0, index_col=0,
... parse_dates=['IND_DAY'])
>>> df = pd.read_excel('data.xlsx', sheet_name='COUNTRIES', index_col=0,
... parse_dates=['IND_DAY'])
Both statements above create the same DataFrame because the sheet_name parameters have the same values. In both cases, sheet_name=0 and sheet_name='COUNTRIES' refer to the same worksheet. The argument parse_dates=['IND_DAY'] tells Pandas to try to consider the values in this column as dates or times.
There are other optional parameters you can use with .read_excel() and .to_excel() to determine the Excel engine, the encoding, the way to handle missing values and infinities, the method for writing column names and row labels, and so on.
SQL Files
Pandas IO tools can also read and write databases. In this next example, you’ll write your data to a database called data.db . To get started, you’ll need the SQLAlchemy package. To learn more about it, you can read the official ORM tutorial. You’ll also need the database driver. Python has a built-in driver for SQLite.
You can install SQLAlchemy with pip:
$ pip install sqlalchemy
You can also install it with Conda:
$ conda install sqlalchemy
Once you have SQLAlchemy installed, import create_engine() and create a database engine:
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite:///data.db', echo=False)
Now that you have everything set up, the next step is to create a DataFrame वस्तु। It’s convenient to specify the data types and apply .to_sql() ।
>>> dtypes = {'POP': 'float64', 'AREA': 'float64', 'GDP': 'float64',
... 'IND_DAY': 'datetime64'}
>>> df = pd.DataFrame(data=data).T.astype(dtype=dtypes)
>>> df.dtypes
COUNTRY object
POP float64
AREA float64
GDP float64
CONT object
IND_DAY datetime64[ns]
dtype: object
.astype() is a very convenient method you can use to set multiple data types at once.
Once you’ve created your DataFrame , you can save it to the database with .to_sql() :
>>> df.to_sql('data.db', con=engine, index_label='ID')
The parameter con is used to specify the database connection or engine that you want to use. The optional parameter index_label specifies how to call the database column with the row labels. You’ll often see it take on the value ID , Id , or id ।
You should get the database data.db with a single table that looks like this:
The first column contains the row labels. To omit writing them into the database, pass index=False to .to_sql() . The other columns correspond to the columns of the DataFrame ।
There are a few more optional parameters. For example, you can use schema to specify the database schema and dtype to determine the types of the database columns. You can also use if_exists , which says what to do if a database with the same name and path already exists:
if_exists='fail'raises a ValueError and is the default.if_exists='replace'drops the table and inserts new values.if_exists='append'inserts new values into the table.
You can load the data from the database with read_sql() :
>>> df = pd.read_sql('data.db', con=engine, index_col='ID')
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
ID
CHN China 1398.72 9596.96 12234.78 Asia NaT
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 None 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaT
DEU Germany 83.02 357.11 3693.20 Europe NaT
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaT
ITA Italy 60.36 301.34 1943.84 Europe NaT
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaT
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
The parameter index_col specifies the name of the column with the row labels. Note that this inserts an extra row after the header that starts with ID . You can fix this behavior with the following line of code:
>>> df.index.name = None
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaT
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 None 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaT
DEU Germany 83.02 357.11 3693.20 Europe NaT
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaT
ITA Italy 60.36 301.34 1943.84 Europe NaT
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaT
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
Now you have the same DataFrame object as before.
Note that the continent for Russia is now None instead of nan . If you want to fill the missing values with nan , then you can use .fillna() :
>>> df.fillna(value=float('nan'), inplace=True)
.fillna() replaces all missing values with whatever you pass to value . Here, you passed float('nan') , which says to fill all missing values with nan ।
Also note that you didn’t have to pass parse_dates=['IND_DAY'] to read_sql() . That’s because your database was able to detect that the last column contains dates. However, you can pass parse_dates if you’d like. You’ll get the same results.
There are other functions that you can use to read databases, like read_sql_table() and read_sql_query() . Feel free to try them out!
Pickle Files
Pickling is the act of converting Python objects into byte streams. Unpickling is the inverse process. Python pickle files are the binary files that keep the data and hierarchy of Python objects. They usually have the extension .pickle or .pkl ।
You can save your DataFrame in a pickle file with .to_pickle() :
>>> dtypes = {'POP': 'float64', 'AREA': 'float64', 'GDP': 'float64',
... 'IND_DAY': 'datetime64'}
>>> df = pd.DataFrame(data=data).T.astype(dtype=dtypes)
>>> df.to_pickle('data.pickle')
Like you did with databases, it can be convenient first to specify the data types. Then, you create a file data.pickle to contain your data. You could also pass an integer value to the optional parameter protocol , which specifies the protocol of the pickler.
You can get the data from a pickle file with read_pickle() :
>>> df = pd.read_pickle('data.pickle')
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaT
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaT
DEU Germany 83.02 357.11 3693.20 Europe NaT
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaT
ITA Italy 60.36 301.34 1943.84 Europe NaT
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaT
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
read_pickle() returns the DataFrame with the stored data. You can also check the data types:
>>> df.dtypes
COUNTRY object
POP float64
AREA float64
GDP float64
CONT object
IND_DAY datetime64[ns]
dtype: object
These are the same ones that you specified before using .to_pickle() ।
As a word of caution, you should always beware of loading pickles from untrusted sources. This can be dangerous! When you unpickle an untrustworthy file, it could execute arbitrary code on your machine, gain remote access to your computer, or otherwise exploit your device in other ways.
Working With Big Data
If your files are too large for saving or processing, then there are several approaches you can take to reduce the required disk space:
- Compress your files
- Choose only the columns you want
- Omit the rows you don’t need
- Force the use of less precise data types
- Split the data into chunks
You’ll take a look at each of these techniques in turn.
Compress and Decompress Files
You can create an archive file like you would a regular one, with the addition of a suffix that corresponds to the desired compression type:
'.gz''.bz2''.zip''.xz'
Pandas can deduce the compression type by itself:
>>>>>> df = pd.DataFrame(data=data).T
>>> df.to_csv('data.csv.zip')
Here, you create a compressed .csv file as an archive. The size of the regular .csv file is 1048 bytes, while the compressed file only has 766 bytes.
You can open this compressed file as usual with the Pandas read_csv() function:
>>> df = pd.read_csv('data.csv.zip', index_col=0,
... parse_dates=['IND_DAY'])
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaT
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaT
DEU Germany 83.02 357.11 3693.20 Europe NaT
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaT
ITA Italy 60.36 301.34 1943.84 Europe NaT
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaT
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
read_csv() decompresses the file before reading it into a DataFrame ।
You can specify the type of compression with the optional parameter compression , which can take on any of the following values:
'infer''gzip''bz2''zip''xz'None
The default value compression='infer' indicates that Pandas should deduce the compression type from the file extension.
Here’s how you would compress a pickle file:
>>>>>> df = pd.DataFrame(data=data).T
>>> df.to_pickle('data.pickle.compress', compression='gzip')
You should get the file data.pickle.compress that you can later decompress and read:
>>> df = pd.read_pickle('data.pickle.compress', compression='gzip')
df again corresponds to the DataFrame with the same data as before.
You can give the other compression methods a try, as well. If you’re using pickle files, then keep in mind that the .zip format supports reading only.
Choose Columns
The Pandas read_csv() and read_excel() functions have the optional parameter usecols that you can use to specify the columns you want to load from the file. You can pass the list of column names as the corresponding argument:
>>> df = pd.read_csv('data.csv', usecols=['COUNTRY', 'AREA'])
>>> df
COUNTRY AREA
0 China 9596.96
1 India 3287.26
2 US 9833.52
3 Indonesia 1910.93
4 Brazil 8515.77
5 Pakistan 881.91
6 Nigeria 923.77
7 Bangladesh 147.57
8 Russia 17098.25
9 Mexico 1964.38
10 Japan 377.97
11 Germany 357.11
12 France 640.68
13 UK 242.50
14 Italy 301.34
15 Argentina 2780.40
16 Algeria 2381.74
17 Canada 9984.67
18 Australia 7692.02
19 Kazakhstan 2724.90
Now you have a DataFrame that contains less data than before. Here, there are only the names of the countries and their areas.
Instead of the column names, you can also pass their indices:
>>>>>> df = pd.read_csv('data.csv',index_col=0, usecols=[0, 1, 3])
>>> df
COUNTRY AREA
CHN China 9596.96
IND India 3287.26
USA US 9833.52
IDN Indonesia 1910.93
BRA Brazil 8515.77
PAK Pakistan 881.91
NGA Nigeria 923.77
BGD Bangladesh 147.57
RUS Russia 17098.25
MEX Mexico 1964.38
JPN Japan 377.97
DEU Germany 357.11
FRA France 640.68
GBR UK 242.50
ITA Italy 301.34
ARG Argentina 2780.40
DZA Algeria 2381.74
CAN Canada 9984.67
AUS Australia 7692.02
KAZ Kazakhstan 2724.90
Expand the code block below to compare these results with the file 'data.csv' :
,COUNTRY,POP,AREA,GDP,CONT,IND_DAY
CHN,China,1398.72,9596.96,12234.78,Asia,
IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15
USA,US,329.74,9833.52,19485.39,N.America,1776-07-04
IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17
BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07
PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14
NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01
BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26
RUS,Russia,146.79,17098.25,1530.75,,1992-06-12
MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16
JPN,Japan,126.22,377.97,4872.42,Asia,
DEU,Germany,83.02,357.11,3693.2,Europe,
FRA,France,67.02,640.68,2582.49,Europe,1789-07-14
GBR,UK,66.44,242.5,2631.23,Europe,
ITA,Italy,60.36,301.34,1943.84,Europe,
ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09
DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05
CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01
AUS,Australia,25.47,7692.02,1408.68,Oceania,
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16
You can see the following columns:
- The column at index
0contains the row labels. - The column at index
1contains the country names. - The column at index
3contains the areas.
Simlarly, read_sql() has the optional parameter columns that takes a list of column names to read:
>>> df = pd.read_sql('data.db', con=engine, index_col='ID',
... columns=['COUNTRY', 'AREA'])
>>> df.index.name = None
>>> df
COUNTRY AREA
CHN China 9596.96
IND India 3287.26
USA US 9833.52
IDN Indonesia 1910.93
BRA Brazil 8515.77
PAK Pakistan 881.91
NGA Nigeria 923.77
BGD Bangladesh 147.57
RUS Russia 17098.25
MEX Mexico 1964.38
JPN Japan 377.97
DEU Germany 357.11
FRA France 640.68
GBR UK 242.50
ITA Italy 301.34
ARG Argentina 2780.40
DZA Algeria 2381.74
CAN Canada 9984.67
AUS Australia 7692.02
KAZ Kazakhstan 2724.90
Again, the DataFrame only contains the columns with the names of the countries and areas. If columns is None or omitted, then all of the columns will be read, as you saw before. The default behavior is columns=None ।
Omit Rows
When you test an algorithm for data processing or machine learning, you often don’t need the entire dataset. It’s convenient to load only a subset of the data to speed up the process. The Pandas read_csv() and read_excel() functions have some optional parameters that allow you to select which rows you want to load:
skiprows: either the number of rows to skip at the beginning of the file if it’s an integer, or the zero-based indices of the rows to skip if it’s a list-like objectskipfooter: the number of rows to skip at the end of the filenrows: the number of rows to read
Here’s how you would skip rows with odd zero-based indices, keeping the even ones:
>>>>>> df = pd.read_csv('data.csv', index_col=0, skiprows=range(1, 20, 2))
>>> df
COUNTRY POP AREA GDP CONT IND_DAY
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
DEU Germany 83.02 357.11 3693.20 Europe NaN
GBR UK 66.44 242.50 2631.23 Europe NaN
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
In this example, skiprows is range(1, 20, 2) and corresponds to the values 1 , 3 , …, 19 . The instances of the Python built-in class range behave like sequences. The first row of the file data.csv is the header row. It has the index 0 , so Pandas loads it in. The second row with index 1 corresponds to the label CHN , and Pandas skips it. The third row with the index 2 and label IND is loaded, and so on.
If you want to choose rows randomly, then skiprows can be a list or NumPy array with pseudo-random numbers, obtained either with pure Python or with NumPy.
Force Less Precise Data Types
If you’re okay with less precise data types, then you can potentially save a significant amount of memory! First, get the data types with .dtypes फिर से:
>>> df = pd.read_csv('data.csv', index_col=0, parse_dates=['IND_DAY'])
>>> df.dtypes
COUNTRY object
POP float64
AREA float64
GDP float64
CONT object
IND_DAY datetime64[ns]
dtype: object
The columns with the floating-point numbers are 64-bit floats. Each number of this type float64 consumes 64 bits or 8 bytes. Each column has 20 numbers and requires 160 bytes. You can verify this with .memory_usage() :
>>> df.memory_usage()
Index 160
COUNTRY 160
POP 160
AREA 160
GDP 160
CONT 160
IND_DAY 160
dtype: int64
.memory_usage() returns an instance of Series with the memory usage of each column in bytes. You can conveniently combine it with .loc[] and .sum() to get the memory for a group of columns:
>>> df.loc[:, ['POP', 'AREA', 'GDP']].memory_usage(index=False).sum()
480
This example shows how you can combine the numeric columns 'POP' , 'AREA' , and 'GDP' to get their total memory requirement. The argument index=False excludes data for row labels from the resulting Series वस्तु। For these three columns, you’ll need 480 bytes.
You can also extract the data values in the form of a NumPy array with .to_numpy() or .values . Then, use the .nbytes attribute to get the total bytes consumed by the items of the array:
>>> df.loc[:, ['POP', 'AREA', 'GDP']].to_numpy().nbytes
480
The result is the same 480 bytes. So, how do you save memory?
In this case, you can specify that your numeric columns 'POP' , 'AREA' , and 'GDP' should have the type float32 . Use the optional parameter dtype to do this:
>>> dtypes = {'POP': 'float32', 'AREA': 'float32', 'GDP': 'float32'}
>>> df = pd.read_csv('data.csv', index_col=0, dtype=dtypes,
... parse_dates=['IND_DAY'])
The dictionary dtypes specifies the desired data types for each column. It’s passed to the Pandas read_csv() function as the argument that corresponds to the parameter dtype ।
Now you can verify that each numeric column needs 80 bytes, or 4 bytes per item:
>>>>>> df.dtypes
COUNTRY object
POP float32
AREA float32
GDP float32
CONT object
IND_DAY datetime64[ns]
dtype: object
>>> df.memory_usage()
Index 160
COUNTRY 160
POP 80
AREA 80
GDP 80
CONT 160
IND_DAY 160
dtype: int64
>>> df.loc[:, ['POP', 'AREA', 'GDP']].memory_usage(index=False).sum()
240
>>> df.loc[:, ['POP', 'AREA', 'GDP']].to_numpy().nbytes
240
Each value is a floating-point number of 32 bits or 4 bytes. The three numeric columns contain 20 items each. In total, you’ll need 240 bytes of memory when you work with the type float32 . This is half the size of the 480 bytes you’d need to work with float64 ।
In addition to saving memory, you can significantly reduce the time required to process data by using float32 instead of float64 in some cases.
Use Chunks to Iterate Through Files
Another way to deal with very large datasets is to split the data into smaller chunks and process one chunk at a time. If you use read_csv() , read_json() or read_sql() , then you can specify the optional parameter chunksize :
>>> data_chunk = pd.read_csv('data.csv', index_col=0, chunksize=8)
>>> type(data_chunk)
<class 'pandas.io.parsers.TextFileReader'>
>>> hasattr(data_chunk, '__iter__')
True
>>> hasattr(data_chunk, '__next__')
True
chunksize defaults to None and can take on an integer value that indicates the number of items in a single chunk. When chunksize is an integer, read_csv() returns an iterable that you can use in a for loop to get and process only a fragment of the dataset in each iteration:
>>> for df_chunk in pd.read_csv('data.csv', index_col=0, chunksize=8):
... print(df_chunk, end='\n\n')
... print('memory:', df_chunk.memory_usage().sum(), 'bytes',
... end='\n\n\n')
...
COUNTRY POP AREA GDP CONT IND_DAY
CHN China 1398.72 9596.96 12234.78 Asia NaN
IND India 1351.16 3287.26 2575.67 Asia 1947-08-15
USA US 329.74 9833.52 19485.39 N.America 1776-07-04
IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17
BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07
PAK Pakistan 205.71 881.91 302.14 Asia 1947-08-14
NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01
BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26
memory: 448 bytes
COUNTRY POP AREA GDP CONT IND_DAY
RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12
MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16
JPN Japan 126.22 377.97 4872.42 Asia NaN
DEU Germany 83.02 357.11 3693.20 Europe NaN
FRA France 67.02 640.68 2582.49 Europe 1789-07-14
GBR UK 66.44 242.50 2631.23 Europe NaN
ITA Italy 60.36 301.34 1943.84 Europe NaN
ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09
memory: 448 bytes
COUNTRY POP AREA GDP CONT IND_DAY
DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05
CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01
AUS Australia 25.47 7692.02 1408.68 Oceania NaN
KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16
memory: 224 bytes
In this example, the chunksize is 8 . The first iteration of the for loop returns a DataFrame with the first eight rows of the dataset only. The second iteration returns another DataFrame with the next eight rows. The third and last iteration returns the remaining four rows.
नोट: You can also pass iterator=True to force the Pandas read_csv() function to return an iterator object instead of a DataFrame object.
In each iteration, you get and process the DataFrame with the number of rows equal to chunksize . It’s possible to have fewer rows than the value of chunksize in the last iteration. You can use this functionality to control the amount of memory required to process data and keep that amount reasonably small.
Conclusion
You now know how to save the data and labels from Pandas DataFrame objects to different kinds of files. You also know how to load your data from files and create DataFrame objects.
You’ve used the Pandas read_csv() and .to_csv() methods to read and write CSV files. You also used similar methods to read and write Excel, JSON, HTML, SQL, and pickle files. These functions are very convenient and widely used. They allow you to save or load your data in a single function or method call.
You’ve also learned how to save time, memory, and disk space when working with large data files:
- Compress or decompress files
- Choose the rows and columns you want to load
- Use less precise data types
- Split data into chunks and process them one by one
You’ve mastered a significant step in the machine learning and data science process! If you have any questions or comments, then please put them in the comments section below.
