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Pickling with Python code and Pandas code

Do both pickling assignment in one Jupyter Notebook file.

Python Pickle steps:

• Download the CSV file.
• Load into a Pandas DataFrame.
• Make the column ‘country’ the index.
• Print the header.
• Using Python code, pickle the DataFrame and name the file: PythonPickle.
• Load back the PythonPickle data into the DataFrame.
• Print the header. (Note both printed headers should match.)

Pandas Pickle steps:

• Download the CSV file (You should already have done this).
• Load into a Pandas DataFrame.
• Make the column ‘country’ the index.
• Print the header.
• Using Pandas code, pickle the DataFrame and name the file: PandasPickle.
• Load back the PandasPickle data into the DataFrame.
• Print the header. (Note both printed headers should match.)

Final step: Make the Notebook readable to anyone. To do this you will need to add titles and narration using markdown language (You will probably have to do a little research on how to use markdown language). Of course, comments in the code assist on in the understanding of what sections and lines of codes do.

drink.csv below

country,beer_servings,spirit_servings,wine_servings,total_litres_of_pure_alcohol,continent
Afghanistan,0,0,0,0.0,Asia
Albania,89,132,54,4.9,Europe
Algeria,25,0,14,0.7,Africa
Andorra,245,138,312,12.4,Europe
Angola,217,57,45,5.9,Africa
Antigua & Barbuda,102,128,45,4.9,North America
Argentina,193,25,221,8.3,South America
Armenia,21,179,11,3.8,Europe
Australia,261,72,212,10.4,Oceania
Austria,279,75,191,9.7,Europe
Azerbaijan,21,46,5,1.3,Europe
Bahamas,122,176,51,6.3,North America
Bahrain,42,63,7,2.0,Asia
Bangladesh,0,0,0,0.0,Asia
Barbados,143,173,36,6.3,North America
Belarus,142,373,42,14.4,Europe
Belgium,295,84,212,10.5,Europe
Belize,263,114,8,6.8,North America
Benin,34,4,13,1.1,Africa
Bhutan,23,0,0,0.4,Asia
Bolivia,167,41,8,3.8,South America
Bosnia-Herzegovina,76,173,8,4.6,Europe
Botswana,173,35,35,5.4,Africa
Brazil,245,145,16,7.2,South America
Brunei,31,2,1,0.6,Asia
Bulgaria,231,252,94,10.3,Europe
Burkina Faso,25,7,7,4.3,Africa
Burundi,88,0,0,6.3,Africa
Cote d'Ivoire,37,1,7,4.0,Africa
Cabo Verde,144,56,16,4.0,Africa
Cambodia,57,65,1,2.2,Asia
Cameroon,147,1,4,5.8,Africa
Canada,240,122,100,8.2,North America
Central African Republic,17,2,1,1.8,Africa
Chad,15,1,1,0.4,Africa
Chile,130,124,172,7.6,South America
China,79,192,8,5.0,Asia
Colombia,159,76,3,4.2,South America
Comoros,1,3,1,0.1,Africa
Congo,76,1,9,1.7,Africa
Cook Islands,0,254,74,5.9,Oceania
Costa Rica,149,87,11,4.4,North America
Croatia,230,87,254,10.2,Europe
Cuba,93,137,5,4.2,North America
Cyprus,192,154,113,8.2,Europe
Czech Republic,361,170,134,11.8,Europe
North Korea,0,0,0,0.0,Asia
DR Congo,32,3,1,2.3,Africa
Denmark,224,81,278,10.4,Europe
Djibouti,15,44,3,1.1,Africa
Dominica,52,286,26,6.6,North America
Dominican Republic,193,147,9,6.2,North America
Ecuador,162,74,3,4.2,South America
Egypt,6,4,1,0.2,Africa
El Salvador,52,69,2,2.2,North America
Equatorial Guinea,92,0,233,5.8,Africa
Eritrea,18,0,0,0.5,Africa
Estonia,224,194,59,9.5,Europe
Ethiopia,20,3,0,0.7,Africa
Fiji,77,35,1,2.0,Oceania
Finland,263,133,97,10.0,Europe
France,127,151,370,11.8,Europe
Gabon,347,98,59,8.9,Africa
Gambia,8,0,1,2.4,Africa
Georgia,52,100,149,5.4,Europe
Germany,346,117,175,11.3,Europe
Ghana,31,3,10,1.8,Africa
Greece,133,112,218,8.3,Europe
Grenada,199,438,28,11.9,North America
Guatemala,53,69,2,2.2,North America
Guinea,9,0,2,0.2,Africa
Guinea-Bissau,28,31,21,2.5,Africa
Guyana,93,302,1,7.1,South America
Haiti,1,326,1,5.9,North America
Honduras,69,98,2,3.0,North America
Hungary,234,215,185,11.3,Europe
Iceland,233,61,78,6.6,Europe
India,9,114,0,2.2,Asia
Indonesia,5,1,0,0.1,Asia
Iran,0,0,0,0.0,Asia
Iraq,9,3,0,0.2,Asia
Ireland,313,118,165,11.4,Europe
Israel,63,69,9,2.5,Asia
Italy,85,42,237,6.5,Europe
Jamaica,82,97,9,3.4,North America
Japan,77,202,16,7.0,Asia
Jordan,6,21,1,0.5,Asia
Kazakhstan,124,246,12,6.8,Asia
Kenya,58,22,2,1.8,Africa
Kiribati,21,34,1,1.0,Oceania
Kuwait,0,0,0,0.0,Asia
Kyrgyzstan,31,97,6,2.4,Asia
Laos,62,0,123,6.2,Asia
Latvia,281,216,62,10.5,Europe
Lebanon,20,55,31,1.9,Asia
Lesotho,82,29,0,2.8,Africa
Liberia,19,152,2,3.1,Africa
Libya,0,0,0,0.0,Africa
Lithuania,343,244,56,12.9,Europe
Luxembourg,236,133,271,11.4,Europe
Madagascar,26,15,4,0.8,Africa
Malawi,8,11,1,1.5,Africa
Malaysia,13,4,0,0.3,Asia
Maldives,0,0,0,0.0,Asia
Mali,5,1,1,0.6,Africa
Malta,149,100,120,6.6,Europe
Marshall Islands,0,0,0,0.0,Oceania
Mauritania,0,0,0,0.0,Africa
Mauritius,98,31,18,2.6,Africa
Mexico,238,68,5,5.5,North America
Micronesia,62,50,18,2.3,Oceania
Monaco,0,0,0,0.0,Europe
Mongolia,77,189,8,4.9,Asia
Montenegro,31,114,128,4.9,Europe
Morocco,12,6,10,0.5,Africa
Mozambique,47,18,5,1.3,Africa
Myanmar,5,1,0,0.1,Asia
Namibia,376,3,1,6.8,Africa
Nauru,49,0,8,1.0,Oceania
Nepal,5,6,0,0.2,Asia
Netherlands,251,88,190,9.4,Europe
New Zealand,203,79,175,9.3,Oceania
Nicaragua,78,118,1,3.5,North America
Niger,3,2,1,0.1,Africa
Nigeria,42,5,2,9.1,Africa
Niue,188,200,7,7.0,Oceania
Norway,169,71,129,6.7,Europe
Oman,22,16,1,0.7,Asia
Pakistan,0,0,0,0.0,Asia
Palau,306,63,23,6.9,Oceania
Panama,285,104,18,7.2,North America
Papua New Guinea,44,39,1,1.5,Oceania
Paraguay,213,117,74,7.3,South America
Peru,163,160,21,6.1,South America
Philippines,71,186,1,4.6,Asia
Poland,343,215,56,10.9,Europe
Portugal,194,67,339,11.0,Europe
Qatar,1,42,7,0.9,Asia
South Korea,140,16,9,9.8,Asia
Moldova,109,226,18,6.3,Europe
Romania,297,122,167,10.4,Europe
Russian Federation,247,326,73,11.5,Asia
Rwanda,43,2,0,6.8,Africa
St. Kitts & Nevis,194,205,32,7.7,North America
St. Lucia,171,315,71,10.1,North America
St. Vincent & the Grenadines,120,221,11,6.3,North America
Samoa,105,18,24,2.6,Oceania
San Marino,0,0,0,0.0,Europe
Sao Tome & Principe,56,38,140,4.2,Africa
Saudi Arabia,0,5,0,0.1,Asia
Senegal,9,1,7,0.3,Africa
Serbia,283,131,127,9.6,Europe
Seychelles,157,25,51,4.1,Africa
Sierra Leone,25,3,2,6.7,Africa
Singapore,60,12,11,1.5,Asia
Slovakia,196,293,116,11.4,Europe
Slovenia,270,51,276,10.6,Europe
Solomon Islands,56,11,1,1.2,Oceania
Somalia,0,0,0,0.0,Africa
South Africa,225,76,81,8.2,Africa
Spain,284,157,112,10.0,Europe
Sri Lanka,16,104,0,2.2,Asia
Sudan,8,13,0,1.7,Africa
Suriname,128,178,7,5.6,South America
Swaziland,90,2,2,4.7,Africa
Sweden,152,60,186,7.2,Europe
Switzerland,185,100,280,10.2,Europe
Syria,5,35,16,1.0,Asia
Tajikistan,2,15,0,0.3,Asia
Thailand,99,258,1,6.4,Asia
Macedonia,106,27,86,3.9,Europe
Timor-Leste,1,1,4,0.1,Asia
Togo,36,2,19,1.3,Africa
Tonga,36,21,5,1.1,Oceania
Trinidad & Tobago,197,156,7,6.4,North America
Tunisia,51,3,20,1.3,Africa
Turkey,51,22,7,1.4,Asia
Turkmenistan,19,71,32,2.2,Asia
Tuvalu,6,41,9,1.0,Oceania
Uganda,45,9,0,8.3,Africa
Ukraine,206,237,45,8.9,Europe
United Arab Emirates,16,135,5,2.8,Asia
United Kingdom,219,126,195,10.4,Europe
Tanzania,36,6,1,5.7,Africa
USA,249,158,84,8.7,North America
Uruguay,115,35,220,6.6,South America
Uzbekistan,25,101,8,2.4,Asia
Vanuatu,21,18,11,0.9,Oceania
Venezuela,333,100,3,7.7,South America
Vietnam,111,2,1,2.0,Asia
Yemen,6,0,0,0.1,Asia
Zambia,32,19,4,2.5,Africa
Zimbabwe,64,18,4,4.7,Africa

Answer 1

The python code to use in the jupyter notebook is:

# importing pandas library
import pandas as pd

# loading the data present in csv file into data frame and
# Making the index as Column country by using index_col='country' while loading data into dataframe
dri_df = pd.read_csv("drinks_csv.csv",index_col='country')

# Gettingt the header of dataframe
dri_df.head()

# below we are pickling(storing) the data frame as PythonPickle.py
dri_df.to_pickle("PythonPickle.pkl")

# Loading back the pythonpickle.pkl data into data frame
unpickled_df = pd.read_pickle("PythonPickle.pkl")

# Showing the header of unpickled data frame
unpickled_df.head()

The jupyter notebook images:

jupyter drinkspickle Last Checkpoint: 20 minutes ago (autosawved) Logout le Edit View Insert l Kenel Widgets Help TrustedPython [conda env Anaconda3] O 凹 In [1]: # importing pandas Library import pandas as pd| In [2]: 共 1oading the data present {n csv file into data frame and # Mok ing the index as Column country by using index-col="country . while loading data into dataframe dri_dfpd.read cs"drinks_csv.csv,index_col-country') In [3]: | # Gettingt the header of dataframe dri_df.head() out [3] beer_servings spirit servings wine_servings total litres of pure_alcohol country afghanistarn albania algeria andorra angola 132 89 25 245 217 54 14 312 45 4.9 0.7 12.4 57 In [4]: # be Low we Qre ρ{ckling(storing) the data frame QS PythonPickle.py dri_df.to pickle(PythonPickle.pkl In [5]: Loading back the pythonpickle.pkl data into data frame unpickled df pd.read pickle("PythonPickle.pkl) In [6]: | # Showing the heQder of tenpickled datQ frame unpickled df.head) out [6]: beer servings spirit servings wine servings res of pure_alcohol

jupyter drinkspickle Last Checkpoint: 20 minutes ago (autosawved) Logout le Edit View Insert l Kenel Widgets Help Trusted 、 1 Python [conda erwAnaconda31 afghanistarn albania algeria andorra angola 25 245 217 54 14 312 45 0.0 4.9 0.7 12.4 138 57 In [4]: # below we are pickling(storing) the data frame as PythonPickle.py dri_df.to pickle("PythonPickle.pkl") In [5]: |# Loading back the pythonpickle.pkl data into data frame unpickled df pd.read pickle("PythonPickle.pkl") In [6]: | # Showing the header of Unpickled data frame unpickled df.head) out [6] beer_servings spirit servings wine_servings total litres of pure_alcohol 0.0 4.9 0.7 afghanistan 132 albania algeria andorra angola 89 25 245 217 54 14 312 45

From the above two images, we can say that the header of data frames from reading CSV file and from pickle is same.

I hope you got the answer and understand it.

Thank YOU:):)

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