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add new column to DataFrame
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Mar 1, 2020
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Section 1
(50 cards)
add new column to DataFrame
df['new'] = df['A'] + df['B']
np.random.randn(x)
generate an array of x random numbers sampled from a standard normal distribution
pop() Function
removes value from dictionary names.pop(Key)
np.zeros(x) and np.ones(x)
creates array of x amount of 0s or 1s
to_csv()
write to csv file ex: df.to_csv('myOutput', index=False)
Examples of plot() variables
color='#FF8C00' linewidth=3 alpha=0.5 linestyle=':' marker='o' markersize=10 markerfacecolor='green' markeredgewidth=3 markeredgecolor='blue'
Pandas
used for series and data frames. ex: import pandas as pd
plt.subplots()
creates multiple graphs on one canvas ex: fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8,2)) axes[0].plot(x,y) axes[1].plot(y,x) plt.tight_layout()
np.arange
np.arange(10,51,2) array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]) loop example: for i in range(3): print(i)
groupby()
groups identical values in DataFrame ex: byGroup = df.groupby('Group') byGroup.sum() or df.groupby('Group').sum()
keys()
Prints all keys in dictionary dict.keys()
eye()
Identity Matrix ex: np.eye(3,3) ([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
Logarithmic Return
log(closing price/previous closing price) ex: PG['log return'] = np.log(PG['Adj Close']/PG['Adj Close'].shift(1))
formatted strings
print (f"This adds {variable} into string")
np.random.rand()
generates a pseudo-random number; if no argument given, returns a float between zero and one
Dictionary syntax
names = {Key1: Value1, Key2: Value2, ...} names[Key1] ##Lookup or Change Keys and Values can be of any type.
np.linspace(x, y, z)
creates z evenly spaced numbers starting with x and ending in y
Series
import pandas as pd pd.Series[data, index]
pandas_datareader
from pandas_datareader import data as web import datetime start = datetime.datetime(2015, 1, 1) end = datetime.datetime(2017, 1, 1) stock = web.DataReader('FB', 'av-daily', start, end, api_key=('1HTZ1QTQW0NQN2LS'))
savefig()
save graph to file ex: fig.savefig('my_picture.png', dpi=200)
pd.read_excel()
read from excel file ex: pd.read_excel('excelFile.xlsx', sheetname='Sheet1')
Annual Rate of Return
# mean of simple returns * 250 days returns = (mydata/mydata.shift(1)) - 1 annual_returns = returns.mean()*250
search multiple conditions in DataFrames
ex: df[ (df['A'] > 0) & (df['B'] > 1) ] :AND df[ (df['A'] > 0) | (df['B'] > 1) ] :OR
fillna()
fills NaN value in DataFrame with another value ex: df.fillna(value='Fill Value') df['A'].fillna(value=df['A'].mean())
unique() and nunique()
returns unique values in DataFrame ex: df['col1'].unique() // returns array of unique values df['col1'].nunique() // returns number of unique values
DataFrame Syntax
import pandas as pd df = pd.DataFrame(array, index=['A','B'], columns=['One','Two','Three'])
join()
joins DataFrames off indexes // use merge to "join" using column indexes ex: df1.join(df2)
NumPy
a scientific computing python package for Arrays and Matrixes import numpy as np
matplotlib basic graph code
fig = plt.figure() axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) axes.plot(x, y) axes.set_xlabel('X Label') axes.set_ylabel('Y Label') axes.set_title('This is my graph')
Simple Rate of Return
(closing price/previous closing price) - 1 ex: PG['simple return'] = (PG['Adj Close']/PG['Adj Close'].shift(1))-1
for loop syntax
fruits = ["apple", "banana", "cherry"] for x in fruits: print(x)
pd.merge()
merges DataFrames with identical keys ex: pd.merge(df1, df2, how='inner', on='key')
value.counts()
returns values and number of times they are in a DataFrame column ex: df['col2'].value_counts()
apply()
apply a function to DataFrame ex: df['col2'].apply(len) ///returns series df['col2'].apply(lamda x: x*2) /// applies user function
sort_values()
sorts DataFrame ex: df.sort_values('col2')
matplotlib import code
import matplotlib.pyplot as plt %matplotlib inline ///allows plotting within notebook %matplotlib notebook ///allows interaction
Working with Lists
list.append('value') // Adds to end list.insert(index, 'value') // Adds del list[index] // Deletes by index list.remove('value') // Removes by value pop_list= list.pop() //Removes last value and reassigns
while loop syntax
price = 4 while price > 0: print(price) price = price - 1 can use break to exit
Python Functions
ex: def functionName(values): .... return x
remove column/row from DataFrame
COLUMN: df.drop('new', axis = 1, inplace = True) ROW: df.drop('new', axis = 0, inplace = True) inplace will remove for good from source
datetime
from datatime import datetime ex: myDateTime = datetime(year, month, day, hour, min, sec)
locate in DataFrame
df['Column Name'] df.loc['Row Name'] df.iloc[row index] df.loc['Row Name', 'Column Name']
dropna()
used to drop row/column from DataFrame with NaN value ex: df.dropna() //row df.dropna(axis=1) // column (thresh) = number of NaN required to remove
reshape()
creates or reshapes matrix x = np.arange(0,9) x.reshape(3,3) array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
Normalization to 100
ex: (mydata/mydata.iloc[0]*100).plot(figsize=(15,6)); plt.show
update() Function
overwrites multiple values in dictionary names.update({Key1: newValue1, Key2: newValue2, ...})
drop()
drops row/column from DataFrame ex: df.drop('col1', axis=1, inplace=true) df.drop('row1', axis=0)
set_index()
set index in DataFrame df.set_index('Column', inplace = true) will overwrite current index column
pd.read_csv()
read from csv file ex: df = pd.read_csv('example.csv')
pd.concat()
glues DataFrames together ex: pd.concat([df1, df2, df3])
Section 2
(4 cards)