<p>The&nbsp;<a href="http://pandas.pydata.org">Pandas</a>&nbsp;library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to <a href="http://matplotlib.org">matplotlib</a> for data visualization and <a href="http://www.numpy.org">NumPy</a>, the fundamental library for scientific computing in Python on which Pandas was built.&nbsp;</p>
<p>The&nbsp;fast, flexible, and expressive Pandas data structures are designed to&nbsp;make real-world data analysis significantly&nbsp;easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming.</p>
<p>That's where this Pandas cheat sheet might come in handy.&nbsp;</p>
<p>It's a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.&nbsp;</p>
<p>As such, you can use it as a handy reference if you&nbsp;are just beginning their data science journey with Pandas or, for those of you who already haven't started yet, you can just use it as a guide to make it easier to learn about and use it.&nbsp;</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/f04456d7-8e61-482f-9cc9-da6f7f25fc9b" target="_blank" rel="noopener"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1621255989/Pandas_Cheat_Sheet_for_Data_Science_in_Python_hqirhp.png" alt="Pandas Cheat Sheet for Data Science in Python" /></a></p>
<p>The Pandas cheat sheet will guide you through the basics of the Pandas library, going from the data structures&nbsp;to I/O, selection, dropping indices or columns, sorting and ranking, retrieving basic information of the data structures you're working with to applying functions and data alignment.</p>
<p>In short, everything that you need to kickstart your data science learning with Python!</p>
<p>Do you want to learn more? Start the <a href="https://www.datacamp.com/courses/intermediate-python-for-data-science/">Intermediate Python For Data Science</a> course for free now or try out our <a href="https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python/">Pandas DataFrame tutorial</a>!&nbsp;</p>
<p>Also, don't miss out on our <a href="https://www.datacamp.com/community/blog/pandas-cheat-sheet-python">Pandas Data Wrangling cheat sheet</a> or our other <a href="https://www.datacamp.com/community/data-science-cheatsheets">data science cheat sheets</a>.&nbsp;</p>
<p><strong>(Click above to download a printable version or read the online version below.)</strong></p>
<h2>Python For Data Science Cheat Sheet: Pandas Basics</h2>
<p>Use the following import convention:</p>
<pre class="language-python"><code>import pandas as pd</code></pre>
<h3>Pandas Data Structures</h3>
<div>
<h3>Series</h3>
<p>A one-dimensional labeled array capable of holding any data type</p>
<pre class="language-python"><code>s = pd.Series([3, -5, 7, 4],  index=['a',  'b',  'c',  'd'])</code></pre>
<table class="fixed">
<tbody>
<tr>
<td>A</td>
<td>3</td>
</tr>
<tr></tr>
<tr></tr>
<tr></tr>
<tr></tr>
</tbody>
</table>
<h3>DataFrame</h3>
<p>A two-dimensional labeled data structure with columns of potentially different types</p>
<pre class="language-python"><code>data = {'Country': ['Belgium',  'India',  'Brazil'],

'Capital': ['Brussels',  'New Delhi',  'Brasilia'],

'Population': [11190846, 1303171035, 207847528]} 

df = pd.DataFrame(data,columns=['Country',  'Capital',  'Population'])</code></pre>
<br />
<table>
<tbody>
<tr>
<th>&nbsp;</th>
<th>Country</th>
<th>Capital</th>
<th>Population</th>
</tr>
<tr>
<td>1</td>
<td>Belgium</td>
<td>Brussels</td>
<td>11190846</td>
</tr>
<tr>
<td>2</td>
<td>India</td>
<td>New Delhi</td>
<td>1303171035</td>
</tr>
<tr>
<td>3</td>
<td>Brazil</td>
<td>Brasilia</td>
<td>207847528</td>
</tr>
</tbody>
</table>
<p>Please note that the first column <strong>1</strong>,<strong>2</strong>,<strong>3</strong> is the index and <strong>Country</strong>,<strong>Capital</strong>,<strong>Population</strong> are the Columns.</p>
<h3>Asking For Help</h3>
<pre class="language-python"><code>help(pd.Series.loc)</code></pre>
<h2>I/O</h2>
<h3>Read and Write to CSV</h3>
<pre class="language-python"><code>pd.read_csv('file.csv', header=None, nrows=5)
df.to_csv('myDataFrame.csv')</code></pre>
<h3>Read multiple sheets from the same file</h3>
<pre class="language-python"><code>xlsx = pd.ExcelFile('file.xls')
df = pd.read_excel(xlsx,  'Sheet1')</code></pre>
<h3>Read and Write to Excel</h3>
<pre class="language-python"><code>pd.read_excel('file.xlsx')
df.to_excel('dir/myDataFrame.xlsx',  sheet_name='Sheet1')</code></pre>
<h3>Read and Write to SQL Query or Database Table</h3>
<p>(read_sql()is a convenience wrapper around read_sql_table() and read_sql_query())</p>
<pre class="language-python"><code>from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
pd.read_sql(SELECT * FROM my_table;, engine)
pd.read_sql_table('my_table', engine)
pd.read_sql_query(SELECT * FROM my_table;', engine)
df.to_sql('myDf', engine)</code></pre>
<h2>Selection</h2>
<h3>Getting</h3>
<p>Get one element</p>
<pre class="language-python"><code>s['b']
-5</code></pre>
<p>Get subset of a DataFrame</p>
<pre class="language-python"><code>df[1:]
Country     Capital   Population
1  India    New Delhi 1303171035
2  Brazil   Brasilia  207847528</code></pre>
<h3>Selecting', Boolean Indexing and Setting</h3>
<h3>By Position</h3>
<p>Select single value by row and and column</p>
<pre class="language-python"><code>df.iloc([0], [0])
'Belgium'
df.iat([0], [0])
'Belgium'</code></pre>
<h3>By Label</h3>
<p>Select single value by row and column labels</p>
<pre class="language-python"><code>df.loc([0],  ['Country'])
'Belgium'
df.at([0],  ['Country'])
'Belgium'</code></pre>
<h3>By Label/Position</h3>
<p>Select single row of subset of rows</p>
<pre class="language-python"><code>df.ix[2]
Country      Brazil
Capital    Brasilia
Population  207847528</code></pre>
<p>Select a single column of subset of columns</p>
<pre class="language-python"><code>df.ix[:, 'Capital']
0     Brussels
1    New Delhi
2     Brasilia</code></pre>
<p>Select rows and columns</p>
<pre class="language-python"><code>df.ix[1, 'Capital']
'New Delhi'</code></pre>
<h3>Boolean Indexing</h3>
<p>Series s where value is not &gt;1</p>
<pre class="language-python"><code>s[~(s &gt; 1)]</code></pre>
<p>s where value is &lt;-1 or &gt;2</p>
<pre class="language-python"><code>s[(s &lt; -1) | (s &gt; 2)]</code></pre>
<p>Use filter to adjust DataFrame</p>
<pre class="language-python"><code>df[df['Population']&gt;1200000000]</code></pre>
<h3>Setting</h3>
<p>Set index a of Series s to 6</p>
<pre class="language-python"><code>s['a'] = 6</code></pre>
<h2>Dropping</h2>
<p>Drop values from rows (axis=0)</p>
<pre class="language-python"><code>s.drop(['a',  'c'])</code></pre>
<p>Drop values from columns(axis=1)</p>
<pre class="language-python"><code>df.drop('Country', axis=1) 
</code></pre>
<h3>Sort and Rank</h3>
<p>Sort by labels along an axis</p>
<pre class="language-python"><code>df.sort_index()</code></pre>
<p>Sort by the values along an axis</p>
<pre class="language-python"><code>df.sort_values(by='Country') </code></pre>
<p>Assign ranks to entries</p>
<pre class="language-python"><code>df.rank()</code></pre>
<h2>Retrieving Series/DataFrame Information</h2>
<h3>Basic Information</h3>
<p>(rows, columns)</p>
<pre class="language-python"><code>df.shape</code></pre>
<p>Describe index</p>
<pre class="language-python"><code>df.index</code></pre>
<p>Describe DataFrame columns</p>
<pre class="language-python"><code>df.columns</code></pre>
<p>Info on DataFrame</p>
<pre class="language-python"><code>df.info()</code></pre>
<p>Number of non-NA values</p>
<pre class="language-python"><code>df.count()</code></pre>
<h3>Summary</h3>
<p>Sum of values</p>
<pre class="language-python"><code>df.sum()</code></pre>
<p>Cumulative sum of values</p>
<pre class="language-python"><code>df.cumsum()</code></pre>
<p>Minimum/maximum values</p>
<pre class="language-python"><code>df.min()/df.max()</code></pre>
<p>Minimum/Maximum index value</p>
<pre class="language-python"><code>df.idxmin()/df.idxmax() </code></pre>
<p>Summary statistics</p>
<pre class="language-python"><code>df.describe()</code></pre>
<p>Mean of values</p>
<pre class="language-python"><code>df.mean()</code></pre>
<p>Median of values</p>
<pre class="language-python"><code>df.median()</code></pre>
<h2>Applying Functions</h2>
<pre class="language-python"><code>f = lambda x: x*2</code></pre>
<p>Apply function</p>
<pre class="language-python"><code>df.apply(f)</code></pre>
<p>Apply function element-wise</p>
<pre class="language-python"><code>df.applynap(f) </code></pre>
<h3>Internal Data Alignment</h3>
<p>NA values are introduced in the indices that don't overlap:</p>
<pre class="language-python"><code>s3 = pd.Series([7, -2, 3],  index=['a',  'c',  'd'])
s + s3
a     10.0
b     NaN
c     5.0
d     7.0</code></pre>
<h3>Arithmetic Operations with Fill Methods</h3>
<p>You can also do the internal data alignment yourself with the help of the fill methods:</p>
<pre class="language-python"><code>s.add(s3, fill_value=0)
a    10.0
b    -5.0
c    5.0
d    7.0
s.sub(s3, fill_value=2)
s.div(s3, fill_value=4)
s.mul(s3, fill_value=3)</code></pre>
<h2>Learn more about pandas</h2>
<p><a href="https://www.datacamp.com/courses/joining-data-with-pandas">Joining Data with pandas</a></p>
<p><a href="https://www.datacamp.com/courses/reshaping-data-with-pandas">Reshaping Data with pandas</a></p>
<p><a href="https://www.datacamp.com/courses/data-manipulation-with-pandas">Data Manipulation with pandas</a></p>
<p><a href="https://www.datacamp.com/courses/writing-efficient-code-with-pandas">Writing Efficient Code with pandas</a></p>
<p>&nbsp;</p>
</div>

A quick guide to the basics of the Python data analysis library Pandas, including code samples.

Pandas Cheat Sheet for Data Science in Python

<p>Most of you who are learning data science with Python will&nbsp;have definitely heard already about&nbsp;scikit-learn, the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.&nbsp;</p>
<p>If you're still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.&nbsp;</p>
<p>That's why DataCamp has created a&nbsp;scikit-learn&nbsp;cheat sheet for those of you who have already started learning about the Python package, but that still want a handy reference sheet. Or, if you still have no idea about how&nbsp;scikit-learn&nbsp;works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.&nbsp;</p>
<p>Either way, we're sure that you're going to find it useful when you're tackling machine learning problems! &nbsp;</p>
<p>This&nbsp;scikit-learn&nbsp;cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you'll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.&nbsp;</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/eb807da5-dce5-4b97-a54d-74e89f14266b" target="_blank" rel="noopener" data-ol-has-click-handler=""><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625837937/Scikit-Learn_Cheat_Sheet-_Python_Machine_Learning_wxjgwe.png" alt="Scikit-Learn Cheat Sheet" /></a></p>
<p>In short, this cheat sheet will kickstart your data science&nbsp;projects: with the help of code examples, you'll have created, validated and tuned your machine learning models in no time. &nbsp;</p>
<p>So what are you waiting for? Time to get started!</p>
<p>(Click above to download a printable version or read the online version below.)&nbsp;</p>
<div id="scoped-content">
<h2>Python For Data Science Cheat Sheet: Scikit-learn</h2>
<p>Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.</p>
<h3>A Basic Example</h3>
<pre class="language-python"><code>from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)</code></pre>
<h3>Loading The Data</h3>
<p>Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices. Other types that are convertible to numeric arrays, such as Pandas DataFrame, are also acceptable.</p>
<pre class="language-python"><code>import numpy as np
X = np.random.random((10,5))
y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])
X[X &lt; 0.7] = 0</code></pre>
<h3>Preprocessing The Data</h3>
<h4>Standardization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)</code></pre>
<h4>Normalization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)</code></pre>
<h4>Binarization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Binarizer
binarizer = Binarizer(threshold=0.0).fit(X)
binary_X = binarizer.transform(X)</code></pre>
<h4>Encoding Categorical Features</h4>
<pre class="language-python"><code>from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
y = enc.fit_transform(y)</code></pre>
<h4>Imputing Missing Values</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Imputer
imp = Imputer(missing_values=0, strategy='mean', axis=0)
imp.fit_transform(X_train)</code></pre>
<h4>Generating Polynomial Features</h4>
<pre class="language-python"><code>from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(5)
oly.fit_transform(X)</code></pre>
<h3>Training And Test Data</h3>
<pre class="language-python"><code>from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)</code></pre>
<h3>Create Your Model</h3>
<h4>Supervised Learning Estimators</h4>
<p>Linear Regression</p>
<pre class="language-python"><code>from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)</code></pre>
<p>Support Vector Machines (SVM)</p>
<pre class="language-python"><code>from sklearn.svm import SVC
svc = SVC(kernel='linear')</code></pre>
<p>Naive Bayes</p>
<pre class="language-python"><code>from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()</code></pre>
<p>KNN</p>
<pre class="language-python"><code>from sklearn import neighbors
knn = neighbors.KNeighborsClassifier(n_neighbors=5)</code></pre>
<p>Unsupervised Learning Estimators</p>
<p>Principal Component Analysis (PCA)</p>
<pre class="language-python"><code>from sklearn.decomposition import PCA
pca = PCA(n_components=0.95)</code></pre>
<p>K Means</p>
<pre class="language-python"><code>from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)</code></pre>
<h3>Model Fitting</h3>
<h4>Supervised learning</h4>
<pre class="language-python"><code>lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)</code></pre>
Unsupervised Learning
<pre class="language-python"><code>k_means.fit(X_train)
pca_model = pca.fit_transform(X_train)</code></pre>
<h3>Prediction</h3>
<p>Supervised Estimators</p>
<pre class="language-python"><code>y_pred = svc.predict(np.random.random((2,5)))
y_pred = lr.predict(X_test)
y_pred = knn.predict_proba(X_test))</code></pre>
<p>Unsupervised Estimators</p>
<pre class="language-python"><code>y_pred = k_means.predict(X_test)</code></pre>
<h3>Evaluate Your Model's Performance</h3>
<h4>Classification Metrics</h4>
<p>Accuracy Score</p>
<pre class="language-python"><code>knn.score(X_test, y_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)</code></pre>
<p>Classification Report</p>
<pre class="language-python"><code>from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)))</code></pre>
<p>Confusion Matrix</p>
<pre class="language-python"><code>from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred)))</code></pre>
<h4>Regression Metrics</h4>
<p>Mean Absolute Error</p>
<pre class="language-python"><code>from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2])
mean_absolute_error(y_true, y_pred))</code></pre>
<p>Mean Squared Error</p>
<pre class="language-python"><code>from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred))</code></pre>
<p>R2&nbsp;Score</p>
<pre class="language-python"><code>from sklearn.metrics import r2_score
r2_score(y_true, y_pred))</code></pre>
<h4>Clustering Metrics</h4>
<p>Adjusted Rand Index</p>
<pre class="language-python"><code>from sklearn.metrics import adjusted_rand_score
adjusted_rand_score(y_true, y_pred))</code></pre>
<p>Homogeneity</p>
<pre class="language-python"><code>from sklearn.metrics import homogeneity_score
homogeneity_score(y_true, y_pred))</code></pre>
<p>V-measure</p>
<pre class="language-python"><code>from sklearn.metrics import v_measure_score
metrics.v_measure_score(y_true, y_pred))</code></pre>
<h4>Cross-Validation</h4>
<pre class="language-python"><code>print(cross_val_score(knn, X_train, y_train, cv=4))
print(cross_val_score(lr, X, y, cv=2))</code></pre>
<h3>Tune Your Model</h3>
<h4>Grid Search</h4>
<pre class="language-python"><code>from sklearn.grid_search import GridSearchCV
params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]}
grid = GridSearchCV(estimator=knn,param_grid=params)
grid.fit(X_train, y_train)
print(grid.best_score_)
print(grid.best_estimator_.n_neighbors)</code></pre>
<h4>Randomized Parameter Optimization</h4>
<pre class="language-python"><code>from sklearn.grid_search import RandomizedSearchCV
params = {"n_neighbors": range(1,5), "weights": ["uniform", "distance"]}
rsearch = RandomizedSearchCV(estimator=knn,
   param_distributions=params,
   cv=4,
   n_iter=8,
   random_state=5)
rsearch.fit(X_train, y_train)
print(rsearch.best_score_)</code></pre>
<h3>Going Further</h3>
<p>Begin with&nbsp;<a href="https://www.datacamp.com/community/tutorials/machine-learning-python/" data-ol-has-click-handler="">our scikit-learn tutorial for beginners</a>, in which you'll learn in an easy, step-by-step way&nbsp;how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values. In addition, you'll make use of Python's data visualization library matplotlib to visualize your results.</p>
<p>&nbsp;</p>
<p>PS. Don't miss our&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/" data-ol-has-click-handler="">Bokeh cheat sheet</a>, the&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/" data-ol-has-click-handler="">Pandas cheat sheet</a>&nbsp;or the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/" data-ol-has-click-handler="">Python cheat sheet for data science</a>.&nbsp;</p>
</div>

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.

Scikit-Learn Cheat Sheet: Python Machine Learning

This cheat sheet guides you through the basics of how data science can help your business, including building your data science team and the common steps in the data science workflow.

Data Science Cheat Sheet for Business Leaders

Download quick points of reference from our cheat sheets blog. There's everything from advanced NLP in Python & the Bokeh library to data.table in R & more.

Data Science Cheat Sheets

Pandas Cheat Sheet for Data Science in Python

Scikit-Learn Cheat Sheet: Python Machine Learning

Data Science Cheat Sheet for Business Leaders