Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python.
Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python. Scikit-Learn comes with many machine learning models that you can use out of the box.
In this cheat sheet, we’ll summarize some of the most common and useful functionality from these libraries. Let’s jump straight in!
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Importing Data
Any kind of data analysis starts with getting hold of some data. Pandas gives you plenty of options for getting data into your Python workbook:
| pd.read_csv(filename) # From a CSV file pd.read_table(filename) # From a delimited text file (like TSV) pd.read_excel(filename) # From an Excel file pd.read_sql(query, connection_object) # Reads from a SQL table/database pd.read_json(json_string) # Reads from a JSON formatted string, URL or file. pd.read_html(url) # Parses an html URL, string or file and extracts tables to a list of dataframes pd.read_clipboard() # Takes the contents of your clipboard and passes it to read_table() pd.DataFrame(dict) # From a dict, keys for columns names, values for data as lists |
Exploring Data
Once you have imported your data into a Pandas dataframe, you can use these methods to get a sense of what the data looks like:
| df.shape() # Prints number of rows and columns in dataframe df.head(n) # Prints first n rows of the DataFrame df.tail(n) # Prints last n rows of the DataFrame df.info() # Index, Datatype and Memory information df.describe() # Summary statistics for numerical columns s.value_counts(dropna=False) # Views unique values and counts df.apply(pd.Series.value_counts) # Unique values and counts for all columns df.describe() # Summary statistics for numerical columns df.mean() # Returns the mean of all columns df.corr() # Returns the correlation between columns in a DataFrame df.count() # Returns the number of non-null values in each DataFrame column df.max() # Returns the highest value in each column df.min() # Returns the lowest value in each column df.median() # Returns the median of each column df.std() # Returns the standard deviation of each column |
Selecting
Often, you might need to select a single element or a certain subset of the data to inspect it or perform further analysis. These methods will come in handy:
| df[col] # Returns column with label col as Series df[[col1, col2]] # Returns Columns as a new DataFrame s.iloc[0] # Selection by position (selects first element) s.loc[0] # Selection by index (selects element at index 0) df.iloc[0,:] # First row df.iloc[0,0] # First element of first column |
Data Cleaning
If you’re working with real world data, chances are you’ll need to clean it up. These are some helpful methods:
| df.columns = ['a','b','c'] # Renames columns pd.isnull() # Checks for null Values, Returns Boolean Array pd.notnull() # Opposite of s.isnull() df.dropna() # Drops all rows that contain null values df.dropna(axis=1) # Drops all columns that contain null values df.dropna(axis=1,thresh=n) # Drops all rows have have less than n non null values df.fillna(x) # Replaces all null values with x s.fillna(s.mean()) # Replaces all null values with the mean (mean can be replaced with almost any function from the statistics section) s.astype(float) # Converts the datatype of the series to float s.replace(1,'one') # Replaces all values equal to 1 with 'one' s.replace([1,3],['one','three']) # Replaces all 1 with 'one' and 3 with 'three' df.rename(columns=lambda x: x + 1) # Mass renaming of columns df.rename(columns={'old_name': 'new_ name'}) # Selective renaming df.set_index('column_one') # Changes the index df.rename(index=lambda x: x + 1) # Mass renaming of index |
Filter, Sort and Group By
Methods for filtering, sorting and grouping your data:
| df[df[col] > 0.5] # Rows where the col column is greater than 0.5 df[(df[col] > 0.5) & (df[col] < 0.7)] # Rows where 0.5 < col < 0.7 df.sort_values(col1) # Sorts values by col1 in ascending order df.sort_values(col2,ascending=False) # Sorts values by col2 in descending order df.sort_values([col1,col2], ascending=[True,False]) # Sorts values by col1 in ascending order then col2 in descending order df.groupby(col) # Returns a groupby object for values from one column df.groupby([col1,col2]) # Returns a groupby object values from multiple columns df.groupby(col1)[col2].mean() # Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics section) df.pivot_table(index=col1, values= col2,col3], aggfunc=mean) # Creates a pivot table that groups by col1 and calculates the mean of col2 and col3 df.groupby(col1).agg(np.mean) # Finds the average across all columns for every unique column 1 group df.apply(np.mean) # Applies a function across each column df.apply(np.max, axis=1) # Applies a function across each row |
Joining and Combining
Methods for combining two dataframes:
| df1.append(df2) # Adds the rows in df1 to the end of df2 (columns should be identical) pd.concat([df1, df2],axis=1) # Adds the columns in df1 to the end of df2 (rows should be identical) df1.join(df2,on=col1,how='inner') # SQL-style joins the columns in df1 with the columns on df2 where the rows for col have identical values. how can be one of 'left', 'right', 'outer', 'inner'<strong> </strong> |
Writing Data
And finally, when you have produced results with your analysis, there are several ways you can export your data:
| df.to_csv(filename) # Writes to a CSV file df.to_excel(filename) # Writes to an Excel file df.to_sql(table_name, connection_object) # Writes to a SQL table df.to_json(filename) # Writes to a file in JSON format df.to_html(filename) # Saves as an HTML table df.to_clipboard() # Writes to the clipboard |
Machine Learning
The Scikit-Learn library contains useful methods for training and applying machine learning models. Our Scikit-Learn tutorial provides more context for the code below.
For a complete list of the Supervised Learning, Unsupervised Learning, and Dataset Transformation, and Model Evaluation modules in Scikit-Learn, please refer to its user guide.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # Import libraries and modules import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import make_pipeline from sklearn.model_selection import GridSearchCV from sklearn.metrics import mean_squared_error, r2_score from sklearn.externals import joblib # Load red wine data. dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv' data = pd.read_csv(dataset_url, sep=';') # Split data into training and test sets y = data.quality X = data.drop('quality', axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123, stratify=y) # Declare data preprocessing steps pipeline = make_pipeline(preprocessing.StandardScaler(), RandomForestRegressor(n_estimators=100)) # Declare hyperparameters to tune hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'], 'randomforestregressor__max_depth': [None, 5, 3, 1]} # Tune model using cross-validation pipeline clf = GridSearchCV(pipeline, hyperparameters, cv=10) clf.fit(X_train, y_train) # Refit on the entire training set # No additional code needed if clf.refit == True (default is True) # Evaluate model pipeline on test data pred = clf.predict(X_test) print r2_score(y_test, pred) print mean_squared_error(y_test, pred) # Save model for future use joblib.dump(clf, 'rf_regressor.pkl') # To load: clf2 = joblib.load('rf_regressor.pkl') |
Conclusion
We’ve barely scratching the surface in terms of what you can do with Python and data science, but we hope this cheatsheet has given you a taste of what you can do!
This post was kindly provided by our friend Kara Tan. Kara is a cofounder of Altitude Labs, a full-service app design and development agency that specializes in data driven design and personalization.
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