R odds and ends R basics

DescriptionRCommentsPython
exam typeclass()type()
vectorc(...)one-dimension arrays
same type
name vectornames(vect)<-c(...)
slicing vectorvect[3]
vect[c(3,5,6)]
starting from 1 compares to 0 in Python
2:5!ERROR! unexpected operator '='includes 5
vect[3:5]!ERROR! illegal character '['
use names as indexvec[c('name1','name2',...)]
calculate averagemean()in Python, have to import other librariesnp.mean()
vectors comparison c(2,3,4,5)>3in Python, have to in numpy, pandas
logical selection vect[c(...)>n]
vect[vect2(logical)]
in Python, pandas is common
matrix matrix()
matrix(1:9, byrow = TRUE, nrow = 3)
two-dimensional
same data type
np.matrix()
Naming a matrixrownames(my_matrix) <- row_names_vector

colnames(my_matrix) <- col_names_vector
dimnames =
list(rowname, columnname)
Sum of values of each rowrowSums(some_matrix)ndarray.sum(axis=1)
df.sum(axis=1)
add column(s) to a matrixbigger<- cbind(matrix1, matrix2, ...)pd.concat([df1,df2],axis=1)
Adding a row(s) to a matrixrbind(matrix1, matrix2, ...)pd.concat([df1,df2],axis=0)
df1.append(df2)
Sum of values of each columnndarray.sum(axis=0)
df.sum(axis=0)
slicing Matrixmatrix[row,col]
my_matrix[1,2]
my_matrix[1:3,2:4]
my_matrix[ ,1]
my_matrix[2, ]
factorsfactor()categorical
Convert vector to factormy_factor<-(vector,
order/ non-ordertemp_vector <- c("High", "Low", "High","Low", "Medium")

factor_temp_vector <- factor(temp_vector, order = TRUE, levels = c("Low", "Medium", "High"))
nominal categorical variable

ordinal categorical variable.
s = pd.Series(["a","b","c","a"], dtype="category")

raw_cat = pd.Categorical(["a","b","c","a"], categories=["b","c","d"], .... ordered=False)
Factor levelslevels()
levels(factor_vector) <- c("name1", "name2",...)
summary()summary(my_var)df.describe()
Series.value_counts()
orderedfactor_speed_vector <-factor(speed_vector,ordered=TRUE,levels=c('slow','fast','insane'))
ordered factor can be compared
data frame head(df)
tail(df)
each column must be same data type
examine structure of a dataframestr(df)
create data framedata.frame(vectors)
slicing df[rows,columns]
df[row2,] entire row2
df[,column3] entire column3
use name slicingdf[2:5, 'name']
df['name', ]
df[ ,'name']
subset()
create
subset(planets_df, diameter<1)
==
planets_df[planets_df[,'diameter']<1,]
sortingorder()
returns ranked index not values
values: a[order(a)]
sorting dfindexes=order(df$column3)

df[indexes, ]
listmy_list <- list(comp1, comp2 ...)
Creating a named listmy_list <- list(name1 = your_comp1,
name2 = your_comp2)
same as abovemy_list <- list(your_comp1, your_comp2)
names(my_list) <- c("name1", "name2")
selecting elements from a listshining_list[["reviews"]]
==
shining_list$reviews
list[[2]][1]
add data to listext_list <- c(my_list , my_val)
comparison& and
| or
! not
double sign only compares the first element
&&
||
if syntax in Rif (condition)
{do sth}
else if (condition)
{do sth}
else
{do sth}
read dataread.table
read.delim
read.csv
read.csv2
hotdogs2 <- read.delim("hotdogs.txt", header = FALSE, col.names = c("type", "calories", "sodium"), colClasses = c("factor", "NULL", "numeric"))
check environmentenvironment(func)
specify func without a namefunction(x){x+1}(2)

=> 3
mean()mean(c(1:9, NA),trim=0.1,na.rm=TRUE)trim -> remove outliers
environment> f<-function () x
> x<-99
> f()
[1] 99
exists()a<-5
exists("a")
TRUE
vector propertiestypeoff()
length()
nun value in RNULL (absent of entire vector)
NA (absent of one value in vector)
check nunis.na()
sequenceseq(1,10)
1:10
merge vectorc(vector1, vector2, singlevalue, ...)
paste()
paste0()
paste() sep=" "
paste0 sep=""
string.join(list)
paste0("year_", 1:5)[1] "year_1" "year_2" "year_3" "year_4" "year_5"
plottinghist(one_dim_data)
hist(df$column)
boxplot(multi_dim_data)
boxplot(df)

loading data case using generators and chunks example not using Pandas

loading data case using generators and chunks

example not using Pandas

This is a study note summary of some courses from DataCamp 🙂

bank case

dataset: World Development Indicators

World bank data

  • Data on world economies for over half a century
    • Indicators
      • Population
      • Electricity consumption
      • CO2 emissions
      • Literacy rates
      • Unemployment
In [2]:
import pandas as pd
!dir
 Volume in drive D is VM
 Volume Serial Number is 023B-9433

 Directory of D:\Dropbox\datacamp\toolbox 2

02/11/2017  04:05 PM    <DIR>          .
02/11/2017  04:05 PM    <DIR>          ..
02/11/2017  03:58 PM    <DIR>          .ipynb_checkpoints
02/11/2017  04:05 PM             1,098 bank case.ipynb
02/11/2017  02:34 AM           303,160 ch1_slides.pdf
02/11/2017  03:28 PM           421,057 ch2_slides.pdf
02/11/2017  04:03 PM           161,340 ch3_slides.pdf
11/22/2016  11:39 AM        30,959,939 kamcord_data.csv
02/11/2017  03:56 PM            11,670 list.ipynb
02/11/2017  03:24 PM            40,789 tool2.ipynb
02/11/2017  03:21 PM         3,909,340 tweets.csv
02/10/2017  01:17 AM         4,092,402 tweets.txt
10/02/2016  01:30 PM            26,623 university_towns.txt
12/21/2016  02:34 PM           139,169 WDI_Country.csv
12/28/2016  03:29 PM           743,579 WDI_CS_Notes.csv
02/01/2017  03:20 PM       187,559,624 WDI_Data.csv
02/01/2017  03:19 PM            36,410 WDI_Description.csv
12/28/2016  03:28 PM        57,084,594 WDI_Footnotes.csv
12/21/2016  02:34 PM         3,512,261 WDI_Series.csv
12/21/2016  02:34 PM            32,781 WDI_ST_Notes.csv
              17 File(s)    289,035,836 bytes
               3 Dir(s)  35,920,375,808 bytes free
In [43]:
f = pd.read_csv('WDI_Data.csv',chunksize=10000)
df = f.next()
df.shape
Out[43]:
(10000, 61)
In [44]:
print df.columns
df=df.iloc[:,:5].dropna()
Index([u'Country Name', u'Country Code', u'Indicator Name', u'Indicator Code',
       u'1960', u'1961', u'1962', u'1963', u'1964', u'1965', u'1966', u'1967',
       u'1968', u'1969', u'1970', u'1971', u'1972', u'1973', u'1974', u'1975',
       u'1976', u'1977', u'1978', u'1979', u'1980', u'1981', u'1982', u'1983',
       u'1984', u'1985', u'1986', u'1987', u'1988', u'1989', u'1990', u'1991',
       u'1992', u'1993', u'1994', u'1995', u'1996', u'1997', u'1998', u'1999',
       u'2000', u'2001', u'2002', u'2003', u'2004', u'2005', u'2006', u'2007',
       u'2008', u'2009', u'2010', u'2011', u'2012', u'2013', u'2014', u'2015',
       u'2016'],
      dtype='object')
In [45]:
df.shape
Out[45]:
(725, 5)
In [48]:
df[df['Indicator Code']=='SP.ADO.TFRT']
Out[48]:
Country Name Country Code Indicator Name Indicator Code 1960
48 Arab World ARB Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 133.555013
1500 Caribbean small states CSS Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 162.871212
2952 Central Europe and the Baltics CEB Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 46.716752
4404 Early-demographic dividend EAR Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 116.406607
5856 East Asia & Pacific EAS Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 66.015974
7308 East Asia & Pacific (excluding high income) EAP Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 75.043631
8760 East Asia & Pacific (IDA & IBRD countries) TEA Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT 76.409849
In [55]:
content = df[df['Indicator Code']=='SP.ADO.TFRT'].iloc[0,]
In [65]:
row = list(content.values)
row
Out[65]:
['Arab World',
 'ARB',
 'Adolescent fertility rate (births per 1,000 women ages 15-19)',
 'SP.ADO.TFRT',
 133.55501327768999]
In [64]:
names = ['CountryName', 'CountryCode', 'IndicatorName', 'IndicatorCode', 'Year', 'Value']

Dictionaries for data science

In [66]:
# Zip lists: zipped_lists
zipped_lists = zip(names, row)

# Create a dictionary: rs_dict
rs_dict = dict(zipped_lists)

# Print the dictionary
print(rs_dict)
{'CountryName': 'Arab World', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'CountryCode': 'ARB', 'Year': 133.55501327768999}

Writing a function

In [68]:
# Define lists2dict()
def lists2dict(list1, list2):
    """Return a dictionary where list1 provides
    the keys and list2 provides the values."""

    # Zip lists: zipped_lists
    zipped_lists = zip(list1, list2)

    # Create a dictionary: rs_dict
    rs_dict = dict(zipped_lists)

    # Return the dictionary
    return rs_dict

# Call lists2dict: rs_fxn
rs_fxn = lists2dict(names, row)

# Print rs_fxn
print(rs_fxn)
{'CountryName': 'Arab World', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'CountryCode': 'ARB', 'Year': 133.55501327768999}

Using a list comprehension

In [81]:
# Print the first two lists in row_lists
print(df.iloc[0,:])
print 
print(df.iloc[1,:])
print 
# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(names, sublist) for sublist in df.values]

# Print the first two dictionaries in list_of_dicts
print(list_of_dicts[0])
print(list_of_dicts[1])
Country Name                                             Arab World
Country Code                                                    ARB
Indicator Name    Adolescent fertility rate (births per 1,000 wo...
Indicator Code                                          SP.ADO.TFRT
1960                                                        133.555
Name: 48, dtype: object

Country Name                                             Arab World
Country Code                                                    ARB
Indicator Name    Age dependency ratio (% of working-age populat...
Indicator Code                                          SP.POP.DPND
1960                                                        87.7992
Name: 55, dtype: object

{'CountryName': 'Arab World', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'CountryCode': 'ARB', 'Year': 133.55501327769}
{'CountryName': 'Arab World', 'IndicatorName': 'Age dependency ratio (% of working-age population)', 'IndicatorCode': 'SP.POP.DPND', 'CountryCode': 'ARB', 'Year': 87.79923459912621}

Turning this all into a DataFrame

In [86]:
# Import the pandas package
import pandas as pd

# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(names, sublist) for sublist in df.values]

# Turn list of dicts into a dataframe: df
df2 = pd.DataFrame(list_of_dicts)

print df2.shape
# Print the head of the dataframe
df2.head()
(725, 5)
Out[86]:
CountryCode CountryName IndicatorCode IndicatorName Year
0 ARB Arab World SP.ADO.TFRT Adolescent fertility rate (births per 1,000 wo... 133.555013
1 ARB Arab World SP.POP.DPND Age dependency ratio (% of working-age populat... 87.799235
2 ARB Arab World SP.POP.DPND.OL Age dependency ratio, old (% of working-age po... 6.635328
3 ARB Arab World SP.POP.DPND.YG Age dependency ratio, young (% of working-age ... 81.024250
4 ARB Arab World ER.FSH.AQUA.MT Aquaculture production (metric tons) 4600.000000

Using Python generators for streaming data

Processing data in chunks

example not using Pandas

  • with open(path) as name:
    do sth
In [90]:
# Open a connection to the file
with open('WDI_Data.csv') as f:

    # Skip the column names
    f.readline()

    # Initialize an empty dictionary: counts_dict
    counts_dict = {}

    # Process only the first 1000 rows
    for j in range(0, 1000):

        # Split the current line into a list: line
        line = f.readline().split(',')

        # Get the value for the first column: first_col
        first_col = line[0]

        # If the column value is in the dict, increment its value
        if first_col in counts_dict.keys():
            counts_dict[first_col] += 1

        # Else, add to the dict and set value to 1
        else:
            counts_dict[first_col] = 1

# Print the resulting dictionary
print(counts_dict)
{'Arab World': 1000}

In the previous exercise, you processed a file line by line for a given number of lines. What if, however, we want to to do this for the entire file?

In this case, it would be useful to use generators. Generators allow users to lazily evaluate data.

  • This concept of lazy evaluation is useful when you have to deal with very large datasets because it lets you generate values in an efficient manner by yielding only chunks of data at a time instead of the whole thing at once.

define a generator function read_large_file() that produces a generator object which yields a single line from a file each time next() is called on it.

In [92]:
# Define read_large_file()
def read_large_file(file_object):
    """A generator function to read a large file lazily."""

    # Loop indefinitely until the end of the file
    while True:

        # Read a line from the file: data
        data = file_object.readline()

        # Break if this is the end of the file
        if not data:
            break

        # Yield the line of data
        yield data

# Open a connection to the file
with open('WDI_Data.csv') as file:

    # Create a generator object for the file: gen_file
    gen_file = read_large_file(file)

    # Print the first three lines of the file
    print(next(gen_file))
    print(next(gen_file))
    print(next(gen_file))
Country Name,Country Code,Indicator Name,Indicator Code,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016

Arab World,ARB,"2005 PPP conversion factor, GDP (LCU per international $)",PA.NUS.PPP.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Arab World,ARB,"2005 PPP conversion factor, private consumption (LCU per international $)",PA.NUS.PRVT.PP.05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

  • You've just created a generator function that you can use to help you process large files.
  • You will process the file line by line, to create a dictionary of the counts of how many times each country appears in a column in the dataset.
  • you'll process the entire dataset!
In [93]:
# Initialize an empty dictionary: counts_dict
counts_dict = {}

# Open a connection to the file
with open('WDI_Data.csv') as file:

    # Iterate over the generator from read_large_file()
    for line in read_large_file(file):

        row = line.split(',')
        first_col = row[0]

        if first_col in counts_dict.keys():
            counts_dict[first_col] += 1
        else:
            counts_dict[first_col] = 1

# Print            
print(counts_dict)
{'Canada': 1452, 'Sao Tome and Principe': 1452, 'Turkmenistan': 1452, 'Lao PDR': 1452, 'Arab World': 1452, 'Lithuania': 1452, 'Cambodia': 1452, 'Switzerland': 1452, 'Ethiopia': 1452, 'Saudi Arabia': 1452, 'OECD members': 1452, 'Swaziland': 1452, 'South Asia': 1452, 'Argentina': 1452, 'Bolivia': 1452, 'Cameroon': 1452, 'Burkina Faso': 1452, 'Bahrain': 1452, 'Middle East & North Africa (IDA & IBRD countries)': 1452, 'Rwanda': 1452, 'South Asia (IDA & IBRD)': 1452, '"Egypt': 1452, 'Japan': 1452, 'Channel Islands': 1452, 'American Samoa': 1452, 'Northern Mariana Islands': 1452, 'Slovenia': 1452, 'East Asia & Pacific (IDA & IBRD countries)': 1452, 'IDA total': 1452, 'Bosnia and Herzegovina': 1452, 'Guinea': 1452, 'Russian Federation': 1452, 'World': 1452, 'St. Lucia': 1452, 'Dominica': 1452, 'Liberia': 1452, 'Maldives': 1452, 'Pakistan': 1452, 'Virgin Islands (U.S.)': 1452, 'Oman': 1452, 'Tanzania': 1452, 'Early-demographic dividend': 1452, 'Cabo Verde': 1452, 'Mauritania': 1452, 'Greenland': 1452, 'Gabon': 1452, 'Monaco': 1452, 'New Zealand': 1452, 'Spain': 1452, 'European Union': 1452, '"Venezuela': 1452, 'Jamaica': 1452, 'Albania': 1452, 'Samoa': 1452, 'Slovak Republic': 1452, 'Kazakhstan': 1452, 'Guam': 1452, 'Uruguay': 1452, 'India': 1452, 'Azerbaijan': 1452, 'Lesotho': 1452, 'Middle East & North Africa': 1452, 'Europe & Central Asia (IDA & IBRD countries)': 1452, 'United Arab Emirates': 1452, 'Latin America & Caribbean': 1452, 'Aruba': 1452, 'Upper middle income': 1452, 'Tajikistan': 1452, 'Pacific island small states': 1452, 'Turkey': 1452, 'Afghanistan': 1452, 'Bangladesh': 1452, 'East Asia & Pacific': 1452, 'Solomon Islands': 1452, 'Turks and Caicos Islands': 1452, 'Palau': 1452, 'San Marino': 1452, 'French Polynesia': 1452, 'France': 1452, 'Syrian Arab Republic': 1452, 'Bermuda': 1452, 'Somalia': 1452, 'Peru': 1452, 'Vanuatu': 1452, 'Nauru': 1452, 'Seychelles': 1452, 'Late-demographic dividend': 1452, "Cote d'Ivoire": 1452, 'West Bank and Gaza': 1452, 'Benin': 1452, 'Other small states': 1452, '"Gambia': 1452, 'Cuba': 1452, 'Montenegro': 1452, 'Low & middle income': 1452, 'Togo': 1452, 'China': 1452, 'Armenia': 1452, 'Jordan': 1452, 'Timor-Leste': 1452, 'Dominican Republic': 1452, '"Hong Kong SAR': 1452, 'Ukraine': 1452, 'Ghana': 1452, 'Tonga': 1452, 'Finland': 1452, 'Colombia': 1452, 'Libya': 1452, 'Cayman Islands': 1452, 'Central African Republic': 1452, 'North America': 1452, 'Liechtenstein': 1452, 'Belarus': 1452, 'British Virgin Islands': 1452, 'Kenya': 1452, 'Sweden': 1452, 'Poland': 1452, 'Bulgaria': 1452, 'Mauritius': 1452, 'Romania': 1452, 'Angola': 1452, 'Central Europe and the Baltics': 1452, 'Chad': 1452, 'South Africa': 1452, 'St. Vincent and the Grenadines': 1452, 'Cyprus': 1452, 'Caribbean small states': 1452, 'Brunei Darussalam': 1452, 'Qatar': 1452, 'Pre-demographic dividend': 1452, 'Middle income': 1452, 'Austria': 1452, 'Vietnam': 1452, 'Mozambique': 1452, 'Uganda': 1452, 'Kyrgyz Republic': 1452, 'Hungary': 1452, 'Niger': 1452, 'Isle of Man': 1452, 'United States': 1452, 'Brazil': 1452, 'Sub-Saharan Africa (IDA & IBRD countries)': 1452, '"Macao SAR': 1452, 'Faroe Islands': 1452, 'Europe & Central Asia (excluding high income)': 1452, 'Panama': 1452, 'Mali': 1452, 'Costa Rica': 1452, 'Luxembourg': 1452, 'St. Kitts and Nevis': 1452, 'Andorra': 1452, 'Norway': 1452, 'Euro area': 1452, 'Gibraltar': 1452, 'Ireland': 1452, 'Italy': 1452, 'Nigeria': 1452, 'Lower middle income': 1452, 'Ecuador': 1452, 'IDA & IBRD total': 1452, 'Australia': 1452, 'Algeria': 1452, 'El Salvador': 1452, 'Tuvalu': 1452, 'IDA only': 1452, 'Guatemala': 1452, 'Czech Republic': 1452, 'Sub-Saharan Africa': 1452, 'Middle East & North Africa (excluding high income)': 1452, 'Chile': 1452, 'Marshall Islands': 1452, 'Belgium': 1452, 'Kiribati': 1452, 'Haiti': 1452, 'Belize': 1452, 'Fragile and conflict affected situations': 1452, 'Sierra Leone': 1452, 'Georgia': 1452, '"Yemen': 1452, 'Denmark': 1452, 'Post-demographic dividend': 1452, 'Puerto Rico': 1452, 'Moldova': 1452, 'Morocco': 1452, 'Croatia': 1452, 'Mongolia': 1452, 'Guinea-Bissau': 1452, 'Thailand': 1452, 'Namibia': 1452, 'Grenada': 1452, 'Latin America & Caribbean (excluding high income)': 1452, 'Iraq': 1452, 'Portugal': 1452, 'Estonia': 1452, 'Kosovo': 1452, 'Mexico': 1452, 'Lebanon': 1452, '"Congo': 2904, 'Uzbekistan': 1452, 'Djibouti': 1452, 'Country Name': 1, 'Antigua and Barbuda': 1452, 'Low income': 1452, 'High income': 1452, 'Burundi': 1452, 'Least developed countries: UN classification': 1452, 'IDA blend': 1452, 'Barbados': 1452, 'Madagascar': 1452, 'Sub-Saharan Africa (excluding high income)': 1452, 'Curacao': 1452, 'Bhutan': 1452, 'Sudan': 1452, 'Nepal': 1452, 'Malta': 1452, '"Micronesia': 1452, 'Netherlands': 1452, '"Bahamas': 1452, '"Macedonia': 1452, 'Kuwait': 1452, 'Europe & Central Asia': 1452, 'United Kingdom': 1452, 'Israel': 1452, 'Indonesia': 1452, 'Malaysia': 1452, 'Iceland': 1452, 'Zambia': 1452, 'Senegal': 1452, 'Papua New Guinea': 1452, 'Malawi': 1452, 'Suriname': 1452, 'Trinidad and Tobago': 1452, 'Zimbabwe': 1452, 'Germany': 1452, 'St. Martin (French part)': 1452, 'East Asia & Pacific (excluding high income)': 1452, 'Philippines': 1452, '"Iran': 1452, 'Eritrea': 1452, 'Small states': 1452, 'New Caledonia': 1452, 'Sri Lanka': 1452, 'Not classified': 1452, 'Latvia': 1452, 'South Sudan': 1452, '"Korea': 2904, 'Guyana': 1452, 'IBRD only': 1452, 'Honduras': 1452, 'Myanmar': 1452, 'Equatorial Guinea': 1452, 'Tunisia': 1452, 'Nicaragua': 1452, 'Singapore': 1452, 'Serbia': 1452, 'Comoros': 1452, 'Latin America & the Caribbean (IDA & IBRD countries)': 1452, 'Sint Maarten (Dutch part)': 1452, 'Greece': 1452, 'Paraguay': 1452, 'Fiji': 1452, 'Botswana': 1452, 'Heavily indebted poor countries (HIPC)': 1452}

Writing an iterator to load data in chunks

In [ ]:
# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv('WDI_Data.csv', chunksize=1000)

# Get the first dataframe chunk: df_urb_pop
df_urb_pop = next(urb_pop_reader)

# Check out the head of the dataframe
print(df_urb_pop.head())

# Check out specific country: df_pop_ceb
df_pop_ceb = df_urb_pop[df_urb_pop['Country Code'] == 'CEB']

# Zip dataframe columns of interest: pops
pops = zip(df_pop_ceb['Total Population'], 
            df_pop_ceb['Urban population (% of total)'])

# Turn zip object into list: pops_list
pops_list = list(pops)

# Print pops_list
print(pops_list)
In [ ]:
# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv('WDI_Data.csv', chunksize=1000)

# Get the first dataframe chunk: df_urb_pop
df_urb_pop = next(urb_pop_reader)

# Check out specific country: df_pop_ceb
df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == 'CEB']

# Zip dataframe columns of interest: pops
pops = zip(df_pop_ceb['Total Population'], 
            df_pop_ceb['Urban population (% of total)'])

# Turn zip object into list: pops_list
pops_list = list(pops)

# Use list comprehension to create new dataframe column 'Total Urban Population'
df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1]) for tup in pops_list]

# Plot urban population data
df_pop_ceb.plot(kind='scatter', x='Year', y='Total Urban Population')
plt.show()
In [ ]:
# Define plot_pop()
def plot_pop(filename, country_code):

    # Initialize reader object: urb_pop_reader
    urb_pop_reader = pd.read_csv(filename, chunksize=1000)

    # Initialize empty dataframe: data
    data = pd.DataFrame()
    
    # Iterate over each dataframe chunk
    for df_urb_pop in urb_pop_reader:
        # Check out specific country: df_pop_ceb
        df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == country_code]

        # Zip dataframe columns of interest: pops
        pops = zip(df_pop_ceb['Total Population'],
                    df_pop_ceb['Urban population (% of total)'])

        # Turn zip object into list: pops_list
        pops_list = list(pops)

        # Use list comprehension to create new dataframe column 'Total Urban Population'
        df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1]) for tup in pops_list]
    
        # Append dataframe chunk to data: data
        data = data.append(df_pop_ceb)

    # Plot urban population data
    data.plot(kind='scatter', x='Year', y='Total Urban Population')
    plt.show()

# Set the filename: fn
fn = 'ind_pop_data.csv'

# Call plot_pop for country code 'CEB'
plot_pop(fn, 'CEB')

# Call plot_pop for country code 'ARB'
plot_pop(fn, 'ARB')

list comprehension and generators

list comprehension and generators

list

list comprehensions and generators

Nested list comprehensions

  • [[output expression] for iterator variable in iterable]
  • Collapse for loops for building lists into a single line
    • Components
      • Iterable
      • Iterator variable (represent members of iterable)
      • Output expression
In [1]:
# Create a 5 x 5 matrix using a list of lists: matrix
matrix = [[col for col in range(5)] for row in range(5)]

# Print the matrix
for row in matrix:
    print(row)
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
In [7]:
pair_2=[(num1, num2) for num1 in range(0, 2) for num2 in range(6, 8)]
pair_2
Out[7]:
[(0, 6), (0, 7), (1, 6), (1, 7)]

Using conditionals in comprehensions

  • [ output expression for iterator variable in iterable if predicate expression ].
In [2]:
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create list comprehension: new_fellowship
new_fellowship = [member for member in fellowship if len(member) >= 7]

# Print the new list
print(new_fellowship)
['samwise', 'aragorn', 'legolas', 'boromir']
In [3]:
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create list comprehension: new_fellowship
new_fellowship = [member if len(member) >= 7 else '' for member in fellowship]

# Print the new list
print(new_fellowship)
['', 'samwise', '', 'aragorn', 'legolas', 'boromir', '']

Dict comprehensions

  • Recall that the main difference between a list comprehension and a dict comprehension is the use of curly braces {} instead of []. Additionally, members of the dictionary are created using a colon :, as in key:value
    • Create dictionaries
    • Use curly braces {} instead of brackets []
In [4]:
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create dict comprehension: new_fellowship
new_fellowship = {member:len(member) for member in fellowship}

# Print the new list
print(new_fellowship)
{'aragorn': 7, 'frodo': 5, 'samwise': 7, 'merry': 5, 'gimli': 5, 'boromir': 7, 'legolas': 7}

Generator expressions

  • Recall list comprehension
    • Use ( ) instead of [ ]
In [9]:
g = (2 * num for num in range(10))
g
Out[9]:
<generator object <genexpr> at 0x0000000004335A20>

List comprehensions vs. generators

  • List comprehension - returns a list
  • Generators - returns a generator object
  • Both can be iterated over
In [13]:
(num for num in range(10*1000000) if num % 2 == 0)
Out[13]:
<generator object <genexpr> at 0x0000000004335E10>

Generator functions

Generator functions are functions that, like generator expressions, yield a series of values, instead of returning a single value. A generator function is defined as you do a regular function, but whenever it generates a value, it uses the keyword yield instead of return.

  • Produces generator objects when called
  • Defined like a regular function - def
  • Yields a sequence of values instead of returning a single value
  • Generates a value with yield keyword
In [15]:
def num_sequence(n):
    
    """Generate values from 0 to n."""
    i = 0
    while i < n:
        yield i
        i += 1
In [17]:
test=num_sequence(7)
print type(test)
<type 'generator'>
In [21]:
next(test)
Out[21]:
3
In [22]:
test.next()
Out[22]:
4

List comprehensions for time-stamped data

the pandas Series

  • single-dimension arrays
  • Extract the column 'created_at' from df and assign the result to tweet_time. Fun fact: the extracted column in tweet_time here is a Series data structure!
  • reate a list comprehension that extracts the time from each row in tweet_time. Each row is a string that represents a timestamp, and you will access the 11th to 18th characters in the string to extract the time. Use entry as the iterator variable and assign the result to tweet_clock_time.
In [27]:
import pandas as pd

df = pd.read_csv('tweets.csv')
    
# Extract the created_at column from df: tweet_time
tweet_time = df['created_at']

# Extract the clock time: tweet_clock_time
tweet_clock_time = [entry[11:19] for entry in tweet_time]

# Print the extracted times
print(tweet_clock_time[:100])
['05:24:51', '05:24:57', '05:25:38', '05:25:42', '05:25:48', '05:25:53', '05:25:58', '05:26:12', '05:26:27', '05:26:30', '05:26:35', '05:26:48', '05:27:56', '05:28:28', '05:28:28', '05:28:40', '05:28:55', '05:30:06', '05:30:18', '05:30:20', '05:30:53', '05:30:55', '05:31:41', '05:32:20', '05:32:23', '05:32:32', '05:34:11', '05:34:17', '05:36:07', '05:38:17', '05:38:26', '05:39:39', '05:39:48', '05:40:07', '05:40:19', '05:40:58', '05:41:06', '05:41:21', '05:41:34', '05:41:51', '05:42:13', '05:42:51', '05:43:20', '05:43:24', '05:43:34', '05:44:36', '05:45:16', '05:45:40', '05:46:38', '05:46:40', '05:46:56', '05:47:07', '05:47:36', '05:47:44', '05:47:50', '05:48:01', '05:48:19', '05:49:10', '05:49:31', '05:49:36', '05:49:39', '05:49:39', '05:49:48', '05:49:52', '05:49:54', '05:50:04', '05:50:07', '05:50:16', '05:50:21', '05:50:35', '05:50:46', '05:50:49', '05:50:49', '05:50:56', '05:51:15', '05:51:26', '05:51:28', '05:51:43', '05:52:27', '05:52:32', '05:52:35', '05:52:45', '05:53:00', '05:53:33', '05:53:37', '05:53:55', '05:53:59', '05:54:14', '05:54:26', '05:54:55', '05:54:59', '05:55:25', '05:55:31', '05:55:39', '05:55:53', '05:55:57', '05:56:02', '05:56:14', '05:56:17', '05:56:29']

Conditional list comprehesions for time-stamped data

  • add a conditional expression to the list comprehension so that you only select the times in which entry[17:19] is equal to '19'
In [28]:
# Extract the created_at column from df: tweet_time
tweet_time = df['created_at']

# Extract the clock time: tweet_clock_time
tweet_clock_time = [entry[11:19] for entry in tweet_time if entry[17:19] == '19']

# Print the extracted times
print(tweet_clock_time)
['05:40:19', '05:48:19', '06:02:19', '06:03:19', '04:56:19', '05:40:19', '05:48:19', '06:02:19', '06:03:19', '03:31:19', '03:54:19', '04:23:19']
In [ ]:
 

Python iterators, loading data in chunks with Pandas

Python iterators

loading data in chunks with pandas

tool2

Iterators, load file in chunks

Iterators vs Iterables

an iterable is an object that can return an iterator

  • Examples: lists, strings, dictionaries, file connections
  • An object with an associated iter() method
  • Applying iter() to an iterable creates an iterator

an iterator is an object that keeps state and produces the next value when you call next() on it.

  • Produces next value with next()
In [30]:
a=[1,2,3,4]
b=iter([1,2,3,4])
c=iter([5,6,7,8])
In [31]:
print a
print b
print next(b),next(b),next(b),next(b)
print list(c)
[1, 2, 3, 4]
<listiterator object at 0x00000000044B5A90>
1 2 3 4
[5, 6, 7, 8]

Iterating over iterables

  • Python 2 does NOT work
  • range() doesn't actually create the list; instead, it creates a range object with an iterator that produces the values until it reaches the limit

    • If range() created the actual list, calling it with a value of 10^100 may not work, especially since a number as big as that may go over a regular computer's memory. The value 10^100 is actually what's called a Googol which is a 1 followed by a hundred 0s. That's a huge number!

      • calling range() with 10^100 won't actually pre-create the list.
In [15]:
# Create an iterator for range(10 ** 100): googol
googol = iter(range(10 ** 100))
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-15-57ef632b6db1> in <module>()
      1 # Create an iterator for range(10 ** 100): googol
----> 2 googol = iter(range(10 ** 100))
      3 

OverflowError: range() result has too many items

Iterating over dictionaries

In [32]:
a={1:9, 'what':'why?'}
In [34]:
for key,value in a.items(): print key,value
1 9
what why?

Iterating over file connections

In [40]:
f = open('university_towns.txt')
type(f)
Out[40]:
file
In [47]:
iter(f)
Out[47]:
<open file 'university_towns.txt', mode 'r' at 0x00000000041F9F60>
In [48]:
iter(f)==f
Out[48]:
True
In [49]:
next(f)
Out[49]:
'Florence (University of North Alabama)\n'
In [50]:
next(iter(f))
Out[50]:
'Jacksonville (Jacksonville State University)[2]\n'

Using enumerate

enumerate() returns an enumerate object that produces a sequence of tuples, and each of the tuples is an index-value pair.

In [65]:
# Create a list of strings: mutants
mutants = ['charles xavier',  'bobby drake', 'kurt wagner',  'max eisenhardt',  'kitty pride']
# Create a list of tuples: mutant_list
mutant_list = list(enumerate(mutants))
# Print the list of tuples
print(mutant_list)
print 
# Unpack and print the tuple pairs
for index1, value1 in enumerate(mutants):
    print(index1, value1)

print "\nChange the start index\n"
for index2, value2 in enumerate(mutants, start=3):
    print(index2, value2)
[(0, 'charles xavier'), (1, 'bobby drake'), (2, 'kurt wagner'), (3, 'max eisenhardt'), (4, 'kitty pride')]

(0, 'charles xavier')
(1, 'bobby drake')
(2, 'kurt wagner')
(3, 'max eisenhardt')
(4, 'kitty pride')

Change the start index

(3, 'charles xavier')
(4, 'bobby drake')
(5, 'kurt wagner')
(6, 'max eisenhardt')
(7, 'kitty pride')

Using zip

zip(), which takes any number of iterables and returns a zip object that is an iterator of tuples.

  • If you wanted to print the values of a zip object, you can convert it into a list and then print it.
  • Printing just a zip object will not return the values unless you unpack it first.

In Python 2 , zip() returns a list

  • Docstring: zip(seq1 [, seq2 [...]]) -> [(seq1[0], seq2[0] ...), (...)]

  • Return a list of tuples, where each tuple contains the i-th element from each of the argument sequences. The returned list is truncated in length to the length of the shortest argument sequence.

In [73]:
aliases = ['prof x', 'iceman', 'nightcrawler', 'magneto', 'shadowcat']
powers = ['telepathy','thermokinesis','teleportation','magnetokinesis','intangibility']

# Create a list of tuples: mutant_data
mutant_data = list(zip(mutants, aliases, powers))

# Print the list of tuples
print(mutant_data)

print 
# Create a zip object using the three lists: mutant_zip
mutant_zip = zip(mutants, aliases, powers)

# Print the zip object
print(type(mutant_zip))

# Unpack the zip object and print the tuple values
for value1, value2, value3 in mutant_zip:
    print(value1, value2, value3)
[('charles xavier', 'prof x', 'telepathy'), ('bobby drake', 'iceman', 'thermokinesis'), ('kurt wagner', 'nightcrawler', 'teleportation'), ('max eisenhardt', 'magneto', 'magnetokinesis'), ('kitty pride', 'shadowcat', 'intangibility')]

<type 'list'>
('charles xavier', 'prof x', 'telepathy')
('bobby drake', 'iceman', 'thermokinesis')
('kurt wagner', 'nightcrawler', 'teleportation')
('max eisenhardt', 'magneto', 'magnetokinesis')
('kitty pride', 'shadowcat', 'intangibility')

Loading data in chunks

  • There can be too much data to hold in memory
  • Solution: load data in chunks!
  • Pandas function: read_csv()
    • Specify the chunk: chunksize
In [78]:
import pandas as pd
from time import time
In [110]:
start = time()

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

print 'used {:.2f} s'.format(time()-start)

print df.shape
df.head(1)
used 0.40 s
(357404, 6)
Out[110]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 0 5be921e9-e2bd-47ac-b1c8-94f7289324a2 APP_CLOSED 2016-09-18 03:06:25 android 2.5.1

explore

In [ ]:
a=pd.read_csv('kamcord_data.csv',chunksize=4)
b=pd.read_csv('kamcord_data.csv',iterator=True)
In [139]:
a.next()
Out[139]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 12 d078c3a4-9a80-4b12-9ca7-95873799f4be APP_CLOSED 2016-09-18 14:11:29 ios 6.4.1
1 13 a1ac31cb-6d06-401a-a33f-66f91abf1550 APP_CLOSED 2016-09-27 16:22:06 ios 6.4.1
2 14 48a70e65-205e-4ab9-9232-3bafa6fb9496 APP_CLOSED 2016-09-19 14:45:08 android 2.5.1
3 15 e8330f1a-eac6-4add-89a1-f3545b8189e7 SHOT_RECORDED 2016-09-18 12:52:17 android 2.5.1
In [140]:
x=a.next()
y=a.next()
In [143]:
y.append(x, ignore_index=True)
Out[143]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 20 d04b2d7a-d847-4790-b8ec-a975e7ba56a4 APP_CLOSED 2016-09-19 04:23:31 android 2.5.1
1 21 8dc251b8-03b6-4671-8780-389cd3bc3004 APP_CLOSED 2016-09-11 14:01:04 ios 6.4.1
2 22 e97f8a1a-bdcd-4d38-ac73-63d2b0105395 APP_CLOSED 2016-09-16 19:08:45 android 2.5.1
3 23 beb48c53-d807-4e1a-b1b6-cc20eebf679c SHOT_RECORDED 2016-09-11 06:30:35 android 2.5.1
4 16 e1f9a1cd-605d-4b94-9dfb-a011f9ec2e0d APP_OPEN 2016-09-25 21:17:22 ios 6.4.1
5 17 95b9becf-fa38-4c4a-b265-8bf2594b911a APP_OPEN 2016-09-24 16:58:35 android 2.6
6 18 19836371-f0f0-4db0-b027-a3fa2d0dbf35 SHOT_RECORDED 2016-09-23 12:15:03 ios 6.4.1
7 19 c39eeee3-6605-4970-95b8-0ddb21c81589 SHOT_RECORDED 2016-09-24 04:26:03 android 2.6
In [144]:
pd.concat([x,y], ignore_index=True)
Out[144]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 16 e1f9a1cd-605d-4b94-9dfb-a011f9ec2e0d APP_OPEN 2016-09-25 21:17:22 ios 6.4.1
1 17 95b9becf-fa38-4c4a-b265-8bf2594b911a APP_OPEN 2016-09-24 16:58:35 android 2.6
2 18 19836371-f0f0-4db0-b027-a3fa2d0dbf35 SHOT_RECORDED 2016-09-23 12:15:03 ios 6.4.1
3 19 c39eeee3-6605-4970-95b8-0ddb21c81589 SHOT_RECORDED 2016-09-24 04:26:03 android 2.6
4 20 d04b2d7a-d847-4790-b8ec-a975e7ba56a4 APP_CLOSED 2016-09-19 04:23:31 android 2.5.1
5 21 8dc251b8-03b6-4671-8780-389cd3bc3004 APP_CLOSED 2016-09-11 14:01:04 ios 6.4.1
6 22 e97f8a1a-bdcd-4d38-ac73-63d2b0105395 APP_CLOSED 2016-09-16 19:08:45 android 2.5.1
7 23 beb48c53-d807-4e1a-b1b6-cc20eebf679c SHOT_RECORDED 2016-09-11 06:30:35 android 2.5.1

1st way of loading data in chunks

In [146]:
start = time()

c=0
for chuck in pd.read_csv('kamcord_data.csv',chunksize=50000):
    if c==0:
        df=chuck
        c+=1
    else:
        df=df.append(chuck, ignore_index=True)
        c+=1
print c

print 'used {:.2f} s'.format(time()-start)

print df.shape
df.head(1)
8
used 0.48 s
(357404, 6)
Out[146]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 0 5be921e9-e2bd-47ac-b1c8-94f7289324a2 APP_CLOSED 2016-09-18 03:06:25 android 2.5.1

2ed way of loading data in chunks

In [149]:
start = time()

want=[]

for chuck in pd.read_csv('kamcord_data.csv',chunksize=50000):
    want.append(chuck)

print len(want)

df=pd.concat(want, ignore_index=True)

print 'used {:.2f} s'.format(time()-start)

print df.shape
df.head(1)
8
used 0.43 s
(357404, 6)
Out[149]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 0 5be921e9-e2bd-47ac-b1c8-94f7289324a2 APP_CLOSED 2016-09-18 03:06:25 android 2.5.1

3rd way of loading data in chunks

In [172]:
start = time()

want=[]

f = pd.read_csv('kamcord_data.csv',iterator = True)

go = True
while go:
    try:
        want.append(f.get_chunk(50000))
    except Exception as e:
        print type(e)
        go = False
    
print len(want)

df=pd.concat(want, ignore_index=True)

print 'used {:.2f} s'.format(time()-start)

print df.shape
df.head(1)
<type 'exceptions.StopIteration'>
8
used 0.43 s
(357404, 6)
Out[172]:
Unnamed: 0 user_id event_name event_time os_name app_version
0 0 5be921e9-e2bd-47ac-b1c8-94f7289324a2 APP_CLOSED 2016-09-18 03:06:25 android 2.5.1

Processing large amounts of Twitter data by chunks

In [183]:
import pandas as pd

# Import package
import json

# Initialize empty list to store tweets: tweets_data
tweets_data = []

# Open connection to file
h=open('tweets.txt','r')

# Read in tweets and store in list: tweets_data
for i in h:
    try:
        print 'O',
        tmp=json.loads(i)
        tweets_data.append(tmp)
    except:
        print 'X',
h.close()


t_df = pd.DataFrame(tweets_data)
print 
print t_df.shape

t_df.to_csv('tweets.csv',index=False, encoding= 'utf-8')
t_df.head(1)
O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O X O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
(615, 33)
Out[183]:
contributors coordinates created_at display_text_range entities extended_entities extended_tweet favorite_count favorited filter_level ... quoted_status_id quoted_status_id_str retweet_count retweeted retweeted_status source text timestamp_ms truncated user
0 None None Fri Feb 10 05:24:51 +0000 2017 NaN {u'user_mentions': [{u'indices': [3, 16], u'sc... NaN NaN 0 False low ... 8.299017e+17 829901721182670849 0 False {u'contributors': None, u'truncated': False, u... <a href="http://twitter.com/#!/download/ipad" ... RT @MeckeringBoy: Does Pauline secretly wear h... 1486704291616 False {u'follow_request_sent': None, u'profile_use_b...

1 rows × 33 columns

Processing large amounts of data by chunks

In [184]:
# Initialize an empty dictionary: counts_dict
counts_dict = {}

# Iterate over the file chunk by chunk
for chunk in pd.read_csv('tweets.csv', chunksize=10):

    # Iterate over the column in dataframe
    for entry in chunk['lang']:
        if entry in counts_dict.keys():
            counts_dict[entry] += 1
        else:
            counts_dict[entry] = 1

# Print the populated dictionary
print(counts_dict)
{'fr': 1, 'en': 597, 'und': 14, 'sv': 2, 'es': 1}

Extracting information for large amounts of Twitter data

  • reusable
  • def func
In [185]:
# Define count_entries()
def count_entries(csv_file, c_size, colname):
    """Return a dictionary with counts of
    occurrences as value for each key."""
    
    # Initialize an empty dictionary: counts_dict
    counts_dict = {}

    # Iterate over the file chunk by chunk
    for chunk in pd.read_csv(csv_file, chunksize=c_size):

        # Iterate over the column in dataframe
        for entry in chunk[colname]:
            if entry in counts_dict.keys():
                counts_dict[entry] += 1
            else:
                counts_dict[entry] = 1

    # Return counts_dict
    return counts_dict

# Call count_entries(): result_counts
result_counts = count_entries('tweets.csv', 10, 'lang')

# Print result_counts
print(result_counts)
{'fr': 1, 'en': 597, 'und': 14, 'sv': 2, 'es': 1}
In [ ]:
 

Python odds and ends scope, filter, reduce

description code comments 
quickly assign values a,b,c = (3,7,12)  unpack
nested functions outer func return inner func
tool1

Python's built-in scope

  • check out Python's built-in scope, which is really just a built-in module called builtins
  • to query builtins, you'll need to import builtins
In [16]:
import builtins
print dir(builtins)
['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException', 'BufferError', 'BytesWarning', 'DeprecationWarning', 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False', 'FloatingPointError', 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError', 'ImportWarning', 'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError', 'None', 'NotImplemented', 'NotImplementedError', 'OSError', 'OverflowError', 'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError', 'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'True', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning', 'WindowsError', 'ZeroDivisionError', '__builtins__', '__doc__', '__file__', '__future_module__', '__name__', '__package__', '__path__', 'abs', 'absolute_import', 'all', 'any', 'apply', 'ascii', 'basestring', 'bin', 'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod', 'cmp', 'coerce', 'compile', 'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod', 'dreload', 'enumerate', 'eval', 'execfile', 'file', 'filter', 'float', 'format', 'frozenset', 'get_ipython', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex', 'id', 'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter', 'len', 'license', 'list', 'locals', 'long', 'map', 'max', 'memoryview', 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property', 'range', 'raw_input', 'reduce', 'reload', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'sys', 'tuple', 'type', 'unichr', 'unicode', 'vars', 'xrange', 'zip']

nested functions

  • return innter function
In [22]:
def raise_val(n):
    
    """Return the inner function."""
    def inner(x):
        """Raise x to the power of n."""
        raised = x ** n
        return raised
    
    return inner
In [25]:
square = raise_val(2)
cube =  raise_val(3)

print square(6), cube(6)
36 216

pass parameters

In [30]:
raise_val(4)(3)
Out[30]:
81

scope searched

  • Local scope
  • Enclosing functions
  • Global
  • Built-in
In [44]:
n=3

def outer():
    """Prints the value of n."""
    n = 1
    def inner():
        n = 2
        print(n)
        
    inner()
    print(n)
In [45]:
outer()
2
1

nested func

  • nesting functions is the idea of a closure
  • This means that the nested or inner function remembers the state of its enclosing scope when called
  • Thus, anything defined locally in the enclosing scope is available to the inner function even when the outer function has finished execution
In [46]:
# Define echo
def echo(n):
    """Return the inner_echo function."""

    # Define inner_echo
    def inner_echo(word1):
        """Concatenate n copies of word1."""
        echo_word = word1 * n
        return echo_word

    # Return inner_echo
    return inner_echo

# Call echo: twice
twice = echo(2)

# Call echo: thrice
thrice = echo(3)

# Call twice() and thrice() then print
print(twice('hello'), thrice('hello'))
('hellohello', 'hellohellohello')
In [48]:
echo(7)('wtf ')
Out[48]:
'wtf wtf wtf wtf wtf wtf wtf '

flexible arguments

Function with variable-length arguments (*args)

In [51]:
# Define gibberish
def gibberish(*notmatter):
    """Concatenate strings in *args together."""

    # Initialize an empty string: hodgepodge
    hodgepodge = ''

    # Concatenate the strings in args
    for word in notmatter:
        hodgepodge += word+ ' '

    # Return hodgepodge
    return hodgepodge

# Call gibberish() with one string: one_word
one_word = gibberish("luke")

# Call gibberish() with five strings: many_words
many_words = gibberish("luke", "leia", "han", "obi", "darth")

# Print one_word and many_words
print(one_word)
print(many_words)
luke 
luke leia han obi darth 

Function with variable-length keyword arguments (**kwargs)

In [55]:
# Define report_status
def report_status(**whatevername):
    """Print out the status of a movie character."""

    print("\nBEGIN: REPORT\n")

    print whatevername
    print 
    # Print a formatted status report
    for key, value in whatevername.items():
        print(key + ": " + value)

    print("\nEND REPORT")

# First call to report_status()
report_status(name="luke", affiliation="jedi", status="missing")

# Second call to report_status()
report_status(name="anakin", affiliation="sith lord", status="deceased")
BEGIN: REPORT

{'status': 'missing', 'affiliation': 'jedi', 'name': 'luke'}

status: missing
affiliation: jedi
name: luke

END REPORT

BEGIN: REPORT

{'status': 'deceased', 'affiliation': 'sith lord', 'name': 'anakin'}

status: deceased
affiliation: sith lord
name: anakin

END REPORT

Map() and lambda functions

In [56]:
# Create a list of strings: spells
spells = ['protego', 'accio', 'expecto patronum', 'legilimens']

# Use map() to apply a lambda function over spells: shout_spells
shout_spells = map(lambda item: item + '!!!', spells)

# Convert shout_spells to a list: shout_spells_list
shout_spells_list = list(shout_spells)

# Convert shout_spells into a list and print it
print(shout_spells_list)
['protego!!!', 'accio!!!', 'expecto patronum!!!', 'legilimens!!!']

Filter() and lambda functions

The function filter() offers a way to filter out elements from a list that doesn't satisfy certain criteria.

  • filter(function or None, sequence) -> list, tuple, or string

  • Return those items of sequence for which function(item) is true. If function is None, return the items that are true. If sequence is a tuple or string, return the same type, else return a list.

In [59]:
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Use filter() to apply a lambda function over fellowship: result
result = filter(lambda member: len(member) > 6, fellowship)


# Convert result to a list: result_list
result_list = list(result)

# Convert result into a list and print it
print(result_list)
['samwise', 'aragorn', 'legolas', 'boromir']
In [69]:
filter(lambda member: len(member) >3, ['1234','234','34567'])
Out[69]:
['1234', '34567']
In [83]:
filter(None, [12>1, 'wtf' if 2>1 else 0, 'aiya' if 3>2 else 7, 'momomo' if 4>5 else -44])
Out[83]:
[True, 'wtf', 'aiya', -44]

Reduce() and lambda functions

The reduce() function is useful for performing some computation on a list and, unlike map() and filter(), returns a single value as a result.

To use reduce(), you must import it from the functools module.

  • reduce(function, sequence[, initial]) -> value
  • Apply a function of two arguments cumulatively to the items of a sequence, from left to right, so as to reduce the sequence to a single value.
  • For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5).
  • If initial is present, it is placed before the items of the sequence in the calculation, and serves as a default when the sequence is empty.
In [86]:
# Import reduce from functools
from functools import reduce

# Create a list of strings: stark
stark = ['robb', 'sansa', 'arya', 'eddard', 'jon']

# Use result() to apply a lambda function over stark: result
result = reduce(lambda item1, item2: item1 +' '+ item2, stark)

# Print the result
print(result)
robb sansa arya eddard jon

error handling

  • raise error
In [88]:
try: '3'+3
except Exception, e: print e
cannot concatenate 'str' and 'int' objects
In [91]:
# Define shout_echo
def shout_echo(word1, echo=1):
    """Concatenate echo copies of word1 and three
    exclamation marks at the end of the string."""

    # Raise an error with raise
    if echo < 0:
        raise ValueError('echo must be greater than 0')

    # Concatenate echo copies of word1 using *: echo_word
    echo_word = word1 * echo

    # Concatenate '!!!' to echo_word: shout_word
    shout_word = echo_word + '!!!'

    # Return shout_word
    return shout_word

# Call shout_echo
try:
    shout_echo("particle", echo=-3)
except Exception, e: 
    print e
echo must be greater than 0
In [96]:
shout_echo("123", echo=-1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-96-44c64dc8c830> in <module>()
----> 1 shout_echo("123", echo=-1)

<ipython-input-91-d6d66ed4753d> in shout_echo(word1, echo)
      6     # Raise an error with raise
      7     if echo < 0:
----> 8         raise ValueError('echo must be greater than 0')
      9 
     10     # Concatenate echo copies of word1 using *: echo_word

ValueError: echo must be greater than 0

use filter(lambda: x ...) in Pandas

In [98]:
# Select retweets from the Twitter dataframe: result

result = filter(lambda x: x[0:2] == 'RT', df['text'])
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-98-ac4d1cb6d465> in <module>()
      1 # Select retweets from the Twitter dataframe: result
      2 
----> 3 result = filter(lambda x: x[0:2] == 'RT', df['text'])

NameError: name 'df' is not defined
In [ ]:
 

 

database python connection basic Sqlalchemy & Pandas

connecting a database through Python

Sqlite

Sqlalchemy

Pandas

basic