Notes on Python3, Git, and a number of useful packages
Flexible input is often denoted with underscoresvarbatim code _to be filled in_
a[#] Select specific element
a[x:y:z] Select elements starting at x up to but not including y at step z. (may be negative to count backward)
-z may be implicit.
-a[x:] slice starting at x
-a[:y] slice up to but not including y
: Select all within dimension
.. For as many dots, represents :,:
open(_file_, _mode_) because this is unsafe to opena and not close, use with preceeding with clause
-'r' read
-'a' append
-'w' write
-'x' create
cd _relative path_ change cwd
pwd print current working directory
ld list contents of directory
git config --global user.name "[*firstname lastname*]" set a name for yourself
git init initialize cwd as a git repository
git clone _url_ retrieve an entire repository from url
git status show modified files in cwd
git add
- . add all files not ignored to the commit and begin tracking
- -a stage all tracked changes to commit
git reset _file_ unstage a file from commit
git diff what is changed but not staged
git diff --staged what is staged but not committed
git commit -m "_descriptive message_" commit staged content with message
git remote add _alias_ _url_ add git url as an alias
git fetch _alias_ fetches down all branches from remote
git merge _alias_/_branch_ marbe a remote branch into current branch and bring it up to date
git push _alias_ _branch_ Transmit local branch commits to the remote repo
git pull fetch and merge any commits from the tracked repo
git log show the commit history of the active branch
The real strength of Python is the access to lots of good packages.
I prefer to use pip to manage and update packages.
pip freeze show what is currently installed in current environment
pip install _package_ install package into current environment
-pip install _package_==_version number_ set installation to version of package
-pip install git+_url_@_branch name_ if a repo is configured to be a package, installs repo as package
pip uninstall _package_
C backend and lots of array and vectorization functions allow this to be ideal for numerical calculation.
Basic object within numpy is a multidimensional array. The 0 axis, or dimension, will be the highest level and each subsequent axis will be the nect dimension in. Length is the number or elements within each dimension. Ex. a 2d Array. The 0 axis has a length 3, the 1 axis has length 5. (or 2 and 5 respectively if we start at 0 pythonically)
a = array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])a = np.array([1, 2, 3, 4], dtype=_type_) Initialize an array with optional type declaration
np.zeros((_shape_)) create an array of zeros to specified shape
np.ones((_shape_)) create an array of ones in shape specified
np.arange(#) create a 1-d array with # elements counting from 0
a.T Transpose list or array
a.shape return shape
a.ndim return number of dimensions
a.reshape(_new shape_) reshape array. note that the number of elements must match
a.flat flatten multidimensional array.
A and B are 2 2x2 arrays.
A*B elementwise multiplication
A@B Matrix multiplication
A.dot(B) dot product
Save/load pickled file: np.save() np.load()
Save/load text file: np.loadtxt() np.savetxt()
np.polyfit(x, y, _degree_)
mean np.mean()
standard deviation np.std()
median np.median()
np.random
.rand(n) n random numbers between 0 and 1
.normal(mean, std, count)
.seed() Helps create repeatable random numbers
skew reports skew
df=pd.DataFrame() Initialize dataframe
df.to_csv('_name_.csv', index=False) Save to csv
df.read_csv('_name_.csv')
df.to_json('_name_.json',orient='table',index=False)
df.to_excel('_name_.xlsx',index=False)
.describe() simple statistics
.head() display first 5 lines
normal Stuff:
plt.subplot(_rows_, _columns_, _current figure_)
ax.grid()
plt.legend()
plt.show()Misc Plots:
plt.scatter(x, y)
plt.hist(_var_, _sins_, alpha=_transparency_, label='_name_')c=Colors
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character color
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``'b'`` blue
``'g'`` green
``'r'`` red
``'y'`` yellow
``'k'`` black
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m=Markers
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character description
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``'.'`` point marker
``'o'`` circle marker
``'s'`` square marker
``'^'`` triangle marker
``'*'`` star marker
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ln=Line Styles
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character description
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``'-'`` solid line style
``'--'`` dashed line style
``'-.'`` dash-dot line style
``':'`` dotted line style
============= ===============================
Interactive html-based interactive plotting.
import plotly.express as pxfig = px.scatter(data,x='x',y='y',color='w',size='x',hover_data=['w'])
fig.show()wrapper for matplotlib that makes things prettier
import seaborn as snsMisc Plots:
sns.pairplot(data[['x','y','z','w']],hue=('w')) # pairplot to observe trends with variables
sns.boxplot(x='w',y='x',data=data) # box plot
sns.violinplot(x='w',y='x',data=data,size=6) # violin plot
sns.jointplot('x','z',data=data,kind="kde") # shows two variables and univariate joint distributions. cool heat map between distributinos of bothcommon scaling methods
s = StandardScaler() Each column is normalized to zero mean and standard deviation of one
fit_transform(x) Fit and transform
transform(x) transform based on another fit
inverse_transform(xs) Scale back to original representation
r2_score(meas, model)
train_test_split split data for training and testing
train,test = train_test_split(ds, test_size=0.2, shuffle=True)
performs analysis with nice summary
import statsmodels.api as sm
xc = sm.add_constant(x) # initialize constant
model = sm.OLS(z,xc).fit()
predictions = model.predict(xc)
model.summary()