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Load and Analyse the dataset | # load positive tweets
positive_tweets = twitter_samples.strings('positive_tweets.json')
positive_tweets[:3]
# load negative tweets
negative_tweets = twitter_samples.strings('negative_tweets.json')
negative_tweets[:3]
## total number of pos and neg tweets
print(f"Total No. of Positive tweets: {len(positive_tweets)}"... | nlp/1. NLP Classification - Logistic Classification.ipynb | rishuatgithub/MLPy | apache-2.0 |
Processing of the data to create word frequencies list | from nltk.corpus import stopwords
import re
def clean_tweet(tweet):
'''
clean the tweet to tokenise, remove stop words and stem the words
'''
stop_words = stopwords.words('english')
#print(f'Total stop words in the vocab: {len(stop_words)}')
tweet = re.sub(r'#','',tweet) ## remove the ... | nlp/1. NLP Classification - Logistic Classification.ipynb | rishuatgithub/MLPy | apache-2.0 |
Model Training | ## Generate the vector word frequency for all of the training tweets
train_X = np.zeros((len(train_data),3))
for i in range(len(train_data)):
train_X[i,:] = extract_features(train_data[i], tweet_freq_vocab)
train_y = train_label
test_X = np.zeros((len(test_data),3))
for i in range(len(test_data)):
test_X[i,:... | nlp/1. NLP Classification - Logistic Classification.ipynb | rishuatgithub/MLPy | apache-2.0 |
Making your own predictions | my_tweet1 = 'i liked my prediction score. happy with the results'
model.predict(extract_features(my_tweet1,tweet_freq_vocab))
my_tweet2 = 'i am sad with the result of the football match'
model.predict(extract_features(my_tweet2,tweet_freq_vocab))
my_tweet3 = 'shame that i couldnt get an entry to the competition'
mode... | nlp/1. NLP Classification - Logistic Classification.ipynb | rishuatgithub/MLPy | apache-2.0 |
TensorBoard の DataFrames データにアクセスする
概要
TensorBoard の主な機能はインタラクティブ GUI ですが、ログれーたの事後分析やカスタム視覚化の作成目的で、TensorBoard に保存されているデータログを プログラムで 読み取るユーザーもいます。
TensorBoard 2.3 は、tensorboard.data.experimental.ExperimentFromDev() でこのようなユースケースをサポートしており、TensorBoard のスカラーログにプログラムを使ってアクセスすることができます。このページでは、この新しい API の基本的な使用方法を実演します。
注意:
... | !pip install tensorboard pandas
!pip install matplotlib seaborn
from packaging import version
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from scipy import stats
import tensorboard as tb
major_ver, minor_ver, _ = version.parse(tb.__version__).release
assert major_ver >= 2 and minor... | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
pandas.DataFrame として TensorBoard スカラーを読み込む
TensorBoard logdir が TensorBoard.dev にアップロードされると、logdir は「実験」となります。各実験には一意の ID が割り当てられており、実験の TensorBoard.dev URL で確認することができます。次のデモでは、https://tensorboard.dev/experiment/c1KCv3X3QvGwaXfgX1c4tg にある TensorBoard.dev を使用しています。 | experiment_id = "c1KCv3X3QvGwaXfgX1c4tg"
experiment = tb.data.experimental.ExperimentFromDev(experiment_id)
df = experiment.get_scalars()
df | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
df は、実験のすべてのスカラーログを含む pandas.DataFrame です。
DataFrame の列は次のとおりです。
run: run（実行）は、元の logdir のサブディレクトリに対応しています。この実験では、run は特定のオプティマイザタイプ（トレーニングハイパーパラメータ）を使用した MNIST データセットのニューラルネットワーク（CNN）の完全なトレーニングに由来しています。この DataFrame は、このような run が複数含まれており、別のオプティマイザタイプの配下にある反復トレーニングに対応しています。
tag: これは、同一の行にある value の意味、つまり値が表現するメトリックが何であ... | print(df["run"].unique())
print(df["tag"].unique()) | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
ピボット（ワイドフォーム）DataFrame を取得する
この実験では、各実行の同じステップ時Iに 2 つのタグ（epoch_loss と epoch_accuracy）が存在します。このため、pivot=True キーワード引数を使用することで、「ワイドフォーム」DataFrame を get_scalars() から直接取得することができます。すべてのタグがワイドフォーム DataFrame の列として含まれているため、このケースを含み、場合によっては操作がより便利になります。
ただし、すべての実行のすべてのタグで統一したステップ値を持つ条件が満たされる場合、pivot=True を使用するとエラーになることに注意してください。 | dfw = experiment.get_scalars(pivot=True)
dfw | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
ワイドフォーム DataFrame には、1 つの「value」列の代わりに、epoch_accuracy と epoch_loss の 2 つのタグ（メトリック）が列として明示的に含まれています。
DataFrame を CSV として保存する
pandas.DataFrame has good interoperability with CSV. You can store it as a local CSV file and load it back later. For example: | csv_path = '/tmp/tb_experiment_1.csv'
dfw.to_csv(csv_path, index=False)
dfw_roundtrip = pd.read_csv(csv_path)
pd.testing.assert_frame_equal(dfw_roundtrip, dfw) | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
カスタム視覚化と統計分析を実行する | # Filter the DataFrame to only validation data, which is what the subsequent
# analyses and visualization will be focused on.
dfw_validation = dfw[dfw.run.str.endswith("/validation")]
# Get the optimizer value for each row of the validation DataFrame.
optimizer_validation = dfw_validation.run.apply(lambda run: run.spli... | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
上記のプロットは、検証精度と検証損失のタイムコースを示し、それぞれの曲線は、あるオプティマイザタイプによる 5 回の実行の平均を示します。seaborn.lineplot() に組み込まれた機能により、それぞれの曲線は、平均に関する ±1 の標準偏差も表示するため、曲線の変動性と 3 つのオプティマイザの差の重要性がわかりやすくなります。この変動性の視覚化は、TensorBoard の GUI ではまだサポートされていません。
最小検証損失が「adam」、「rmsprop」、および「sgd」オプティマイザ間で大きく異なるという仮説を調べるため、それぞれのオプティマイザにおける最小検証損失の DataFrame を抽出します。
そして... | adam_min_val_loss = dfw_validation.loc[optimizer_validation=="adam", :].groupby(
"run", as_index=False).agg({"epoch_loss": "min"})
rmsprop_min_val_loss = dfw_validation.loc[optimizer_validation=="rmsprop", :].groupby(
"run", as_index=False).agg({"epoch_loss": "min"})
sgd_min_val_loss = dfw_validation.loc[optimi... | site/ja/tensorboard/dataframe_api.ipynb | tensorflow/docs-l10n | apache-2.0 |
Preparing data set sweep
First, we're going to define the data sets that we'll sweep over. As the simulated novel taxa dataset names depend on how the database generation notebook was executed, we must define the variables used to create these datasets. If you modified any variables in that notebook, set these same var... | iterations = 3
data_dir = join(project_dir, "data", analysis_name)
# databases is a list of names given as dictionary keys in the second
# cell of the database generation notebook. Just list the names here.
databases = ['B1-REF', 'F1-REF']
# Generate a list of input directories
(dataset_reference_combinations, referen... | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
Preparing the method/parameter combinations and generating commands
Now we set the methods and method-specific parameters that we want to sweep. Modify to sweep other methods. Note how method_parameters_combinations feeds method/parameter combinations to parameter_sweep() in the cell below.
Assignment Using QIIME 1 or ... | method_parameters_combinations = { # probabalistic classifiers
'rdp': {'confidence': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9, 1.0]},
# global alignment classifiers
'uclust': {'min_consensus_fraction': [0.51, 0.76, 1.... | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
Now enter the template of the command to sweep, and generate a list of commands with parameter_sweep().
Fields must adhere to following format:
{0} = output directory
{1} = input data
{2} = output destination
{3} = reference taxonomy
... | command_template = "source activate qiime1; source ~/.bashrc; mkdir -p {0} ; assign_taxonomy.py -v -i {1} -o {0} -r {2} -t {3} -m {4} {5} --rdp_max_memory 16000"
commands = parameter_sweep(data_dir, results_dir, reference_dbs,
dataset_reference_combinations,
... | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
As a sanity check, we can look at the first command that was generated and the number of commands generated. | print(len(commands))
commands[0] | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
Finally, we run our commands. | Parallel(n_jobs=4)(delayed(system)(command) for command in commands) | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
BLAST+ | method_parameters_combinations = {
'blast+' : {'p-evalue': [0.001],
'p-maxaccepts': [1, 10],
'p-min-id': [0.80, 0.97, 0.99],
'p-min-consensus': [0.51, 0.99]}
}
command_template = ("mkdir -p {0}; "
... | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
VSEARCH | method_parameters_combinations = {
'vsearch' : {'p-maxaccepts': [1, 10],
'p-min-id': [0.80, 0.99],
'p-min-consensus': [0.51, 0.99]}
}
command_template = ("mkdir -p {0}; "
"qiime feature-classifier vsearch --i-query {1}... | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
Move result files to repository
Add results to the short-read-taxa-assignment directory (e.g., to push these results to the repository or compare with other precomputed results in downstream analysis steps). The precomputed_results_dir path and methods_dirs glob below should not need to be changed unless if substantial... | precomputed_results_dir = join(project_dir, "data", "precomputed-results", analysis_name)
method_dirs = glob(join(results_dir, '*', '*', '*', '*'))
move_results_to_repository(method_dirs, precomputed_results_dir) | ipynb/novel-taxa/taxonomy-assignment.ipynb | nbokulich/short-read-tax-assignment | bsd-3-clause |
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div> | %%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js') | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Getting and Knowing your Data
Task: load the following file as a data frame | fn = r"data/drinks.csv"
# Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: See the first 10 entries | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Which country has the highest alcohol consumption (total litres of pure alcohol)? | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Groupby
Task: Which continent drinks most beer on average? | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: List all unique continents. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Which countries have missing values in the continent column? | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Set "the" missing continent with a name of your choice. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: For each continent print "the" statistics (summary stats using "df.describe()") for wine consumption. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Print the median alcoohol consumption per continent for every column | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Print the mean, min and max values for spirit consumption. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: GroupBy Continent and create a Boxplot. (Hint: using e.g. figsize=(12, 9), rot=90 might help with legibility.) | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Concatenate, Merge & Join
Task: Import the first dataset cars1 and cars2. Assign each to a to a variable called cars1 and cars2. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: It seems our first dataset has some unnamed blank columns, fix cars1. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Join cars1 and cars2 into a single DataFrame called cars | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Apply (interspersed)
Task: Create function that returns the first word of the string in the "car" column, the manufacturer name. Use the "apply" method to create a new column in the DataFrame. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Consider the following DataFrames for the next exercises | df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
... | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Concatenate the three DataFrames along the rows. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: How many missing values (NaNs) are produced if you concatenate along the other axis (appending the columns)? | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Let's consider another data set to do some more Merge, Join & Concatenate exerciseses | raw_data_1 = {
'subject_id': ['1', '2', '3', '4', '5'],
'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}
raw_data_2 = {
'subject_id': ['4', '5', '6', '7', '8', '9', '10'],
'first_name': ['Alice', 'Ayoung... | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Join the two dataframes, data1 and data2, along rows and assign all_data. Make sure that the row index is unique. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Join the two dataframes, data1 and data2, along columns and assing to all_data_col. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Merge all_data and data3 along the subject_id value. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: How many test_ids have missing values in the first or last name column? | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Merge only the data that has the same 'subject_id' in both data1 and data2. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Transform
The transform method returns an object that is indexed the same (same size) as the one being grouped.
Task: Given a DataFrame with a column of group IDs, 'groups', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean. | # Write your answer here
# Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Use groupby in conjunction with transform across multiple columns: We want to group by one to n columns and apply a function on these groups across two columns.
1. Calculate the sum of a and b and assign it to a column named e.
2. Group by 'c' and d, and calculate the sum of e | df = pd.DataFrame({'a':[1,2,3,4,5,6],
'b':[1,2,3,4,5,6],
'c':['q', 'q', 'q', 'q', 'w', 'w'],
'd':['z','z','z','o','o','o']})
# Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Normalize (standardize) the data by calculating the z-score. Group the data by year and calculate the z-score per group. z = (value - mean) / standard_deviation
<div style="font-size: 150%;">
$$z=\frac{x-\mu}{\sigma}$$
</div> | index = pd.date_range('10/1/1999', periods=1100)
ser = pd.Series(np.random.normal(0.5, 2, 1100), index=index)
ser = ser.rolling(window=100,min_periods=100).mean().dropna()
# Answer:
key = lambda x: x.year
zscore = lambda x: (x - x.mean()) / x.std()
transformed = ser.groupby(key).transform(zscore) | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check. Calculate the mean and standard deviation within each group. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Visually compare the original and transformed data sets. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Pivot
Task: Let's reshape this small example DataFrame of ICD10 codes. Each person has different code-associations. Only positive associations are listed. Transform (reshape) the DataFrame to a wide format (one column per code) that lists positive and negative (missing) associations as Booleans. | df = pd.DataFrame({"Person": ["a", "a", "a", "b", "c", "c"], "Code": ["D99", "E32", "A41", "D99", "D99", "A41"]}, columns=["Person", "Code"])
df
# Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Combine DataFrames
Task: In the data/microbiome subdirectory, there are 9 spreadsheets of microbiome data that was acquired from high-throughput RNA sequencing procedures, along with a 10th file that describes the content of each. Write code that imports each of the data spreadsheets and combines them into a single Dat... | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
GroupBy Titanic data
Load the dataset in titanic.xls. It contains data on all the passengers that travelled on the Titanic.
Task:
Women and children first?
Use the groupby method to calculate the proportion of passengers that survived by sex.
Calculate the same proportion, but by class and sex.
Create age categories: ... | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Let's plot the number of survivors grouped by sex and passenger class. | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Let's also look at the deaths (and not only at the survivors) within the groups and create a stacked Barplot of survivers vs. deaths grouped by sex and passenger-class (as before).
1. Convert the "survived" column to boolean values
2. Compute the cross tabulation (a.k.a. contingency table) of passenger-class and ... | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
Task: Another way of comparing the groups is to look at the survival rate, by adjusting for the number of people in each group.
Create a stacked, horizontal Barplot of the adjusted death counts
1. Sum the death_counts per passenger-class and sex, and convert to data type float (for Python 2.x division purposes).
2. Com... | # Write your answer here | Exercises_part_B.ipynb | dblyon/PandasIntro | mit |
As seen, when we assign a lambda expression to a label we can use it. Note that at default lambda expressions return the expected type of whatever it is handling. If you send in a number and do a numerical operation, you will receive back a number, a string a string, etc.
Below, we want to create more complex lambda ex... | # Even or Odd lambda
even_odd = lambda x: x % 2 == 0 | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
As we can see, the lambda expressions returns the number we expected. How do we return a True or False value? Note: we can't use if statements or returns. This limits the power of the lambda. However, we can have lambda expressions return True or False, still. Observe the following syntax. | even_odd = lambda x: True if x % 2 == 0 else False
even_odd(9) | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
Soon, we will learn about creating our own classes, and eventually data structures. When we learn how to make our own data structures, we'll rewrite what are "conventionally" (take it lightly) known as a "magic methods". These methods are not called upon explicitly, but are triggered by some internal action that Python... | # script that "converts" a tuple to a list
some_tup = ("[", 3, 4, "hello", "]")
x = lambda tup: ", ".join(str(item) for item in tup)
print(x(some_tup)) | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
Somewhat scrappy, but let's just say it's pretty close to looking like an actual list. Strings are not mutable so we can't just add a bracket at the beginning or right at the end. There are certain ways to make this possible, however. Operations like this are better suited for functions, anyways.
Lambdas as Parameters
... | sorted([4, 2, 8, 5, 2, 9]) | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
However, we have much more power with the key. The key accepts a form of some type of filter. We can send in a lambda expression to change what sorted means to us. What if we wanted the even numbers to be at the end? | sorted([4, 2, 8, 5, 2, 9], key=lambda x: x%2 == 0) | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
What if we wanted the numbers to be reversed, such as in descending order? | sorted([1, 5, 2, 5, 2, 9, 4], reverse=True)
lst = [1, 3, 4,5]
lst[::-1] | Functions and Methods/Lambda Expressions.ipynb | mohsinhaider/pythonbootcampacm | mit |
TO GET STARTED, CLICK "CELL" IN THE MENU BAR ABOVE, THEN SELECT "RUN ALL" | from SQL_support_code import * | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Structure and Formatting Query Basics:
Indentations and Returns:
Mostly arbitrary in SQL
Usually for readability
Capitalization:
Convention to put keywords (functions, clauses) in CAPS
Consistency is best
Order of Clauses:
Very strict
Not all clauses need to be present in a query, but when they are present, ... | describe_differences | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
These are the names of the tables in our mini SQLite database:
sales_table
car_table
salesman_table
cust_table
Start by looking at the columns and their data types in the sales_table. | run('''
PRAGMA TABLE_INFO(sales_table)
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Rewrite the query to look at the other tables: | run('''
PRAGMA TABLE_INFO(sales_table)
''')
#print(describe_cheat) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Different RDBMS have different datatypes available:
- Oracle: http://docs.oracle.com/cd/B10501_01/appdev.920/a96624/03_types.htm
- MySQL:
- Numeric: http://dev.mysql.com/doc/refman/5.0/en/numeric-type-overview.html
- Date/time: http://dev.mysql.com/doc/refman/5.0/en/date-and-time-type-overview.html
- String/text: ht... | run('''
SELECT
*
FROM
sales_table
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Write a query to select all columns from the car_table: | run('''
SELECT NULL
''')
#print(select_cheat1) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
SELECT COLUMN:
SELECT
column_a, # comma-separate multiple columns
column_b
FROM
table_name
Instead of using an asterisk for "all columns", you can specify a particular column or columns: | run('''
SELECT
model_id,
revenue
FROM
sales_table
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Write a query to select model_id and model from the car_table: | run('''
SELECT NULL
''')
#print(select_cheat2) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
One more quick note on the basics of SELECT - technically you can SELECT a value without using FROM to specify a table. You could just tell the query exactly what you want to see in the result-set. If it's a number, you can write the exact number. If you are using various characters, put them in quotes.
See the query b... | run('''
SELECT
4,
5,
7,
'various characters or text'
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
SELECT DISTINCT VALUES IN COLUMNS:
SELECT
DISTINCT column_a # returns a list of each unique value in column_a
FROM
table_name
Use DISTINCT to return unique values from a column
More on DISTINCT: http://www.w3schools.com/sql/sql_distinct.asp
The query below pulls each distinct value from the model_id colu... | run('''
SELECT
DISTINCT model_id
FROM
sales_table
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Use DISTINCT to select unqiue values from the salesman_id column in sales_table. Delete DISTINCT and rerun to see the effect. | run('''
SELECT NULL
''')
#print(select_cheat3) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
WHERE
SELECT
column_a
FROM
table_name
WHERE
column_a = x # filters the result-set to rows where column_a's value is exactly x
A few more options for the where clause:
WHERE column_a = 'some_text' # put text in quotations. CAPITALIZATION IS IMPORTANT
WHERE column_a != x # filters the result... | run('''
SELECT
*
FROM
sales_table
WHERE
payment_type = 'cash'
AND model_id = 46
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Rewrite the query to return rows where payment_type is NOT cash, and the model_id is either 31 or 36
- Extra: Try changing 'cash' to 'Cash' to see what happens. | run('''
SELECT NULL
''')
#print(where_cheat1) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Using BETWEEN, rewrite the query to return rows where the revenue was between 24,000 and 25,000: | run('''
SELECT NULL
''')
#print(where_cheat2) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
WHERE column LIKE:
SELECT
column_a
FROM
table_name
WHERE
column_a LIKE '%text or number%' # Filters the result_set to rows where that text or value can be found, with % standing in as a wildcard
LIKE lets you avoid issues with capitalization in quotes, and you can use % as a wildcard to stand in for... | run('''
SELECT
*
FROM
sales_table
WHERE
payment_type LIKE 'Cas%'
''').head() | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Be careful with LIKE though - it can't deal with extra characters or mispellings: | run('''
SELECT
*
FROM
sales_table
WHERE
payment_type LIKE 'ces%'
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
LIKE and % will also return too much if you're not specific enough. This returns both 'cash' and 'finance' because both have a 'c' with some letters before or after: | run('''
SELECT
*
FROM
sales_table
WHERE
payment_type LIKE '%c%'
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
You can use different wildcards besides % to get more specific. An underscore is a substitute for a single letter or character, rather than any number. The query below uses 3 underscores after c to get 'cash': | run('''
SELECT
*
FROM
sales_table
WHERE
payment_type LIKE 'c___'
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Say you can't remember the model of the car you're trying to look up. You know it's "out"...something. Outcast? Outstanding? Write a query to return the model_id and model from the car_table and use LIKE to help you search: | run('''
SELECT NULL
''')
#print(where_cheat3) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
ORDER BY
SELECT
column_a
FROM
table_name
WHERE # optional
column_a = x
ORDER BY # sorts the result-set by column_a
column_a DESC # DESC is optional. It sorts results in descending order (100->1) instead of ascending (1->100)
Without an ORDER BY clause, the defa... | run('''
SELECT
*
FROM
sales_table
ORDER BY
revenue DESC
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Rewrite the query above to look at the sticker_price of cars from the car_table in descending order: | run('''
SELECT NULL
''')
#print(order_cheat) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
LIMIT
SELECT
column_a
FROM
table_name
WHERE
columna_a = x # optional
ORDER BY
column_a # optional
LIMIT # Limits the result-set to N rows
N
LIMIT just limits the number of rows in your result set
More on LIMIT: http://www.w3schools.com/sql/sql_top.asp
The ability t... | limit_differences | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
The query below limits the number of rows to 5 results. Change it to 10 to get a quick sense of what we're doing here: | run('''
SELECT
*
FROM
sales_table
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
ALIASES
SELECT
T.column_a AS alias_a # creates a nickname for column_a, and states that it's from table_name (whose alias is T)
FROM
table_name AS T # creates a nickname for table_name
WHERE
alias_a = z # refer to an alias in the WHERE clause
ORDER BY
alias_a # ref... | run('''
SELECT
model_id AS Model_of_car,
revenue AS Rev_per_car
FROM
sales_table
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
You can use an alias in the ORDER BY and WHERE clauses now. Write a query to:
- pull the model_id and revenue for each transaction
- give model_id the alias "Model"
- give revenue the alias "Rev"
- limit the results to only include rows where the model_id id 36, use the alias in the WHERE clause
- order the results by ... | run('''
SELECT NULL
''')
#print(alias_cheat) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
You can also assign an alias to a table, and use the alias to tell SQL which table the column is coming from. This isn't of much use when you're only using one table, but it will come in handy when you start using multiple tables.
Below,the sales_table has the alias "S". Read "S.model_id" as "the model_id column from S... | run('''
SELECT
S.model_id,
S.revenue
FROM
sales_table AS S
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
JOINS
SELECT
*
FROM
table_x
JOIN table_y # use JOIN to add the second table
ON table_x.column_a = table_y.column_a # use ON to specify which columns correspond on each table
Joining tables is the most fundamental and useful part ... | run('''
SELECT
*
FROM
sales_table
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Now the first few rows of the car_table: | run('''
SELECT
*
FROM
car_table
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
These tables are related. There's a column named "model_id" in the sales_table and a "model_id" in the car_table - but the column names don't need to be the same, what's important is that the values in the sales_table's model_id column correspond to the values in the car_table's model_id column.
You can join these tab... | run('''
SELECT
*
FROM
sales_table
JOIN car_table ON sales_table.model_id = car_table.model_id
LIMIT 10
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Write a query to join the cust_table to the sales_table, using the customer_id columns in both tables as the key: | run('''
SELECT NULL
''')
#print(join_cheat1) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Rewrite the query from above, but instead of selecting all columns, specify just the customer gender and the revenue: | run('''
SELECT NULL
''')
#print(join_cheat2) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Rewrite the query from above, but this time select the customer_id, gender, and revenue:
- You'll probably hit an error at first. Try to use what you've learned about this structure "table_x.column_a" to fix the issue. Why do you think you need to use this? | run('''
SELECT NULL
''')
#print(join_cheat3) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
A column with the name customer_id appears in both the cust_table and the sales_table. SQL doesn't know which one you want to see. You have to tell it from which table you want the customer_id.
This can be important when columns in different tables have the same names but totally unrelated values.
Look at the sales_ta... | run('''
SELECT
*
FROM
sales_table
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Above, there's a column called "id".
Now look at the salesman_table again: | run('''
SELECT
*
FROM
salesman_table
LIMIT 5
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
There's a column named "id" in the salesman_table too. However, it doesn't look like those IDs correspond to the sales_table IDs. In fact, it's the salesman_id column in the sales_table that corresponds to the id column in the salesman_table. More often than not, your tables will use different names for corresponding c... | run('''
SELECT NULL
''')
#print(join_cheat4) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Practice applying this "table_x.column_a" format to all columns in the SELECT clause when you are joining multiple tables, since multiple tables frequenty use the same column names even when they don't correspond.
It's common to use single-letter aliases for tables to make queries shorter. Take a look at the query bel... | run('''
SELECT
S.customer_id,
C.gender,
S.revenue
FROM
sales_table AS S
JOIN cust_table AS C on S.customer_id = C.customer_id
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Join the sales_table (assign it the alias S) and salesman_table (alias SM) again.
- Select the id and salesman_id column from the sales_table
- Also, select the id column from the salesman_table
- Optional: assign aliases to the columns in the SELECT clause to make the result-set easier to read | run('''
SELECT NULL
''')
#print(join_cheat5) | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
Different Types of Joins
There are different types of joins you can do according to your needs. Here's a helpful way to visualize your options: http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
However, not all types of joins are compatible with SQLite and MySQL. The table below breaks down c... | join_differences | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
So far, we've just done a simple join, also called an "inner join". To illustrate different types of joins, we're going to use a different "database" for the following lesson. First, let's take a look at each one: | run('''
SELECT
*
FROM
Dog_Table
''')
run('''
SELECT
*
FROM
Cat_Table
''') | Code/SQL/SQL_Intro_DBcopy.ipynb | ky822/Data_Bootcamp | mit |
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