Pandas - Kaggle

Solve short hands-on challenges to perfect your data manipulation skills.

Creating, Reading and Writing

In this tutorial, you will learn how to create your own data, along with how to work with data that already exists.

import pandas as pd

Objects

There are two core objects in pandas: the DataFrame and the Series.

pd.DataFrame() constructor to generate these DataFrame objects. The syntax for declaring a new one is a dictionary whose keys are the column names (later Yes/No; Bob/Sue in example), and whose values are a list of entries. This is the standard way of constructing a new DataFrame.

# A DataFrame is a table. 
# It contains an array of individual entries, each of which has a certain value. 
# Each entry corresponds to a row (or record) and a column.

pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})

Out:

  	Yes 	No
0 	50 	131
1 	21 	2

# DataFrame entries are not limited to integers. 
pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']})

Out:

      Bob 	          Sue
0 	I liked it. 	Pretty good.
1 	It was awful. 	Bland.

The dictionary-list constructor assigns values to the column labels, but just uses an ascending count from 0 (0, 1, 2, 3, ...) for the row labels. Sometimes this is OK, but oftentimes we will want to assign these labels ourselves:

pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 
              'Sue': ['Pretty good.', 'Bland.']},
             index=['Product A', 'Product B'])

Out:

          	Bob 	        Sue
Product A 	I liked it. 	Pretty good.
Product B 	It was awful. 	Bland.

Series

A Series, by contrast, is a sequence of data values. If a DataFrame is a table, a Series is a list. And in fact you can create one with nothing more than a list:

pd.Series([1, 2, 3, 4, 5])

Out:

0    1
1    2
2    3
3    4
4    5
dtype: int64

You can assign column values to the Series the same way as before, using an index parameter. However, a Series does not have a column name, it only has one overall name:

pd.Series([30, 35, 40], index=['2015 Sales', '2016 Sales', '2017 Sales'], name='Product A')

Out:

2015 Sales    30
2016 Sales    35
2017 Sales    40
Name: Product A, dtype: int64

It's helpful to think of a DataFrame as actually being just a bunch of Series "glued together".

Let's now set aside our toy datasets and see what a real dataset looks like when we read it into a DataFrame. We'll use the pd.read_csv() function to read the data into a DataFrame. This goes thusly:

wine_reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv")
wine_reviews.shape #to check how large the resulting DataFrame

Out:

(129971, 14)

This means 129971 records in each row in 14 columns separated, which means 1820000 entries.

useful short commands

wine_reviews.head() - gives fst 5 row of dataset
wine_reviews.head() - can have more than 30 optinoal parameter
- index_col=0 parameter to set fst column as index: wine_reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
animals.to_csv("cows_and_goats.csv") - save animals DataFrame into cows_and_goats.csvfile on the disk

Indexing, Selecting & Assigning

One of the first things you need to learn in working with data in Python is how to go about selecting the data points relevant to you quickly and effectively

desc = reviews['description'] and desc = reviews.description both gives the same result: the description column from reviews and assign the result to the variable desc.
reviews.description.iloc[0] and reviews.description.loc[0] and reviews.description[0] gives te same: the first value from the description column of reviews. Both loc and iloc are row-first, column-second.
reviews.iloc[0] gives back the first row of reviews dataset
reviews.description.iloc[:10] and reviews.description.head(10) and reviews.loc[:9, "description"] are the same, and give back the first 10 values from the description column in reviews
sample_reviews = reviews.iloc[[1,2,3,5,8],:] - Select the records with index labels 1, 2, 3, 5, and 8, assigning the result to the variable sample_reviews
df = reviews.loc[[0,1,10,100], ['country', 'province', 'region_1', 'region_2']] - Create a variable df containing the country, province, region_1, and region_2 columns of the records with the index labels 0, 1, 10, and 100
df = reviews.loc[:99,['country','variety']] - Create a variable df containing the country and variety columns of the first 100 records. In this case df.iloc[0:1000] will return 1000 entries, while df.loc[0:1000] return 1001 of them!
italian_wines = reviews[reviews.country == 'Italy'] - Create a DataFrame italian_wines containing reviews of wines made in Italy
top_oceania_wines = reviews.loc[(reviews.country.isin(['Australia', 'New Zealand'])) & (reviews.points >= 95)] - Create a DataFrame top_oceania_wines containing all reviews with at least 95 points (out of 100) for wines from Australia or New Zealand.
reviews.loc[reviews.price.notnull()] - ˛isnull (and its companion notnull). These methods let you highlight values which are (or are not) empty (NaN).

It's worth knowing that negative numbers can be used in selection. This will start counting forwards from the end of the values reviews.iloc[-5:]

Label-based selection derives its power from the labels in the index. Critically, the index we use is not immutable. We can manipulate the index in any way we see fit. reviews.set_index("title")

Summary Functions and Maps

reviews.points.describe() gives dataser's summary, also possible to use on columns of dataset
.mean() get the mean of the row, ex: reviews.points.mean()
.unique() get the unique values, so let's filter the duplicate ones, ex: reviews.taster_name.unique()
.value_counts() how often the unique values appear, ex: reviews.taster_name.value_counts() => out: Roger Voss 25514

Maps

A map is a term, borrowed from mathematics, for a function that takes one set of values and "maps" them to another set of values. In data science we often have a need for creating new representations from existing data, or for transforming data from the format it is in now to the format that we want it to be in later.

There are two mapping methods that you will use often.

Note that map() and apply() return new, transformed Series and DataFrames, respectively. They don't modify the original data they're called on.

`map()`

The function you pass to map() should expect a single value from the Series (a point value, in the above example), and return a transformed version of that value.

example Output

# map() is the first, and slightly simpler one.
# For example, suppose that we wanted to remean
# the scores the wines received to 0. 
#
# We can do this as follows:

review_points_mean = reviews.points.mean()
reviews.points.map(lambda p: p - review_points_mean)

0        -1.447138
1        -1.447138
            ...   
129969    1.552862
129970    1.552862
Name: points, Length: 129971, dtype: float64

Create variable centered_price containing a version of the price column with the mean price subtracted.

centered_price = reviews.price.map(lambda p: p-reviews.price.mean())

# of course this is equal to 
# centered_price = reviews.price - reviews.price.mean()

0               NaN
1        -20.363389
            ...    
129969    -3.363389
129970   -14.363389
Name: price, Length: 129971, dtype: float64

`apply()`

apply() is the equivalent method if we want to transform a whole DataFrame by calling a custom method on each row.

If we had called reviews.apply() with axis='index', then instead of passing a function to transform each row, we would need to give a function to transform each column.

def remean_points(row):
  row.points = row.points - review_points_mean
  return row

reviews.apply(remean_points, axis='columns')

#  score of 95 or higher counts as 3 stars, 
# a score of at least 85 but less than 95 is 2 stars. 
# Any other score is 1 star.
#
# any wines from Canada should automatically get 3 stars,
# regardless of points.
#
# Create a series star_ratings with the number of stars 
# corresponding to each review in the dataset

def stars(row):
    if row.country == 'Canada':
        return 3
    elif row.points >= 95:
        return 3
    elif row.points >= 85:
        return 2
    else:
        return 1

star_ratings = reviews.apply(stars, axis='columns')

0         2
1         2
         ..
129969    2
129970    2
Length: 129971, dtype: int64

Grouping and Sorting

often we want to group our data, and then do something specific to the group the data is in

Groupwise analysis

One function we've been using heavily thus far is the value_counts() function. We can replicate what value_counts() does by doing the following:

reviews.points

0         87
1         87
2         87
3         87
4         87
          ..
129966    90
129967    90
129968    90
129969    90
129970    90
Name: points, Length: 129971, dtype: int64

reviews.groupby('points').points.count()

points
80     397
81     692
      ... 
99      33
100     19
Name: points, Length: 21, dtype: int64

groupby() created a group of reviews which allotted the same point values to the given wines. Then, for each of these groups, we grabbed the points() column and counted how many times it appeared. value_counts() is just a shortcut to this groupby() operation.

For example, here's one way of selecting the name of the first wine reviewed from each winery in the dataset: reviews.groupby('winery').apply(lambda df: df.title.iloc[0])

For even more fine-grained control, you can also group by more than one column. For an example, here's how we would pick out the best wine by country and province: reviews.groupby(['country', 'province']).apply(lambda df: df.loc[df.points.idxmax()])

`agg()`

Lets you run a bunch of different functions on your DataFrame simultaneously. For example, we can generate a simple statistical summary of the dataset as follows:

reviews.groupby(['country']).price.agg([len, min, max])

Out

            	len 	min 	max
country 			
Argentina 	3800 	4.0 	230.0
Armenia 	2 	14.0 	15.0
   ...     	... 	... 	...
Ukraine 	14 	6.0 	13.0
Uruguay 	109 	10.0 	130.0

43 rows × 3 columns

Multi-indexes

groupby() is slightly different in the fact that, depending on the operation we run, it will sometimes result in what is called a multi-index. A multi-index differs from a regular index in that it has multiple levels. For example:

countries_reviewed = reviews.groupby(['country', 'province']).description.agg([len])
countries_reviewed

Out:

	      	                    len
country     province 	
Argentina   Mendoza Province  3264
            Other             536
...         ...               ...
Uruguay     San Jose          3
            Uruguay           24

The use cases for a multi-index are detailed alongside instructions on using them in the MultiIndex / Advanced Selection section of the pandas documentation.

However, in general the multi-index method you will use most often is the one for converting back to a regular index, the reset_index() method:

countries_reviewed.reset_index()

Out:

	country 	province              len
0 	Argentina 	Mendoza Province  3264
1 	Argentina 	Other             536
... 	...       ...               ...
423 	Uruguay 	San Jose          3
424 	Uruguay 	Uruguay           24

425 rows × 3 columns

Sorting

grouping returns data in index order, not in value order
To get data in the order want it in we can sort it ourselves. The sort_values() method is handy for this.
also we can set descending sort by ascending=False in sort_values() argument

countries_reviewed = countries_reviewed.reset_index()
countries_reviewed.sort_values(by='len')

Out:

	    country 	province 	           len
179 	Greece 	Muscat of Kefallonian  1
192 	Greece 	Sterea Ellada          1
... 	... 	...                      ...
415 	US 	Washington                 8639
392 	US 	California                 36247

425 rows × 3 columns

sort by more than one column at a time:

countries_reviewed.sort_values(by=['country', 'len'])

Out:

      country     province           len
1     Argentina   Other              536
0     Argentina   Mendoza Province   3264
... 	...         ...                ...
424   Uruguay     Uruguay            24
419   Uruguay     Canelones          43

425 rows × 3 columns

more in exercise-grouping-and-sorting.ipynb

Data Types and Missing Values

Dtypes

The data type for a column in a DataFrame or a Series is known as the dtype. You can use the dtype() property to grab the type of a specific column. Alternatively, the dtypes property returns the dtype of every column in the DataFrame: reviews.dtypes.

It's possible to convert a column of one type into another wherever such a conversion makes sense by using the astype() function. For example, we may transform the points column from its existing int64 data type into a float64 data type: reviews.points.astype('float64')

Missing data

To select NaN entries you can use pd.isnull() (or its companion pd.notnull()). Pandas provides a really handy method for this problem: fillna(). reviews.region_2.fillna("Unknown") replace each NaN with an "Unknown".

.replace("@kerinokeefe", "@kerino") replace @kerinokeefe to @kerino.

more in exercise-data-types-and-missing-values.ipynb

Renaming and Combining

Oftentimes data will come to us with column names, index names, or other naming conventions that we are not satisfied with.

rename()
- lets you change index names and/or column names. ex: to change the points column in our dataset to score, we would do: reviews.rename(columns={'points': 'score'})
- rename index or column values by specifying a index or column keyword parameter, respectively: reviews.rename(index={0: 'firstEntry', 1: 'secondEntry'})
set_index() rename indexes more convenient
rename_axis() rename a row and a column index, ex: reviews.rename_axis("wines", axis='rows').rename_axis("fields", axis='columns')

When performing operations on a dataset, we will sometimes need to combine different DataFrames and/or Series in non-trivial ways.

merge() - join() -
concat() - simplest combining method

Concat different databases with same structure:

canadian_youtube = pd.read_csv("../input/youtube-new/CAvideos.csv")
british_youtube = pd.read_csv("../input/youtube-new/GBvideos.csv")

pd.concat([canadian_youtube, british_youtube])

same:

left = canadian_youtube.set_index(['title', 'trending_date'])
right = british_youtube.set_index(['title', 'trending_date'])

left.join(right, lsuffix='_CAN', rsuffix='_UK')

more in exercise-renaming-and-combining.ipynb

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README.md

README.md

Pandas - Kaggle

Creating, Reading and Writing

Objects

Series

useful short commands

Indexing, Selecting & Assigning

Summary Functions and Maps

Maps

`map()`

`apply()`

Grouping and Sorting

Groupwise analysis

`agg()`

Multi-indexes

Sorting

Data Types and Missing Values

Dtypes

Missing data

Renaming and Combining

Files

README.md

Latest commit

History

README.md

File metadata and controls

Pandas - Kaggle

Creating, Reading and Writing

Objects

Series

useful short commands

Indexing, Selecting & Assigning

Summary Functions and Maps

Maps

map()

apply()

Grouping and Sorting

Groupwise analysis

agg()

Multi-indexes

Sorting

Data Types and Missing Values

Dtypes

Missing data

Renaming and Combining

`map()`

`apply()`

`agg()`