Matplotlib Cheat Sheet

*x_values*
**days = [0, 1, 2, 3, 4, 5, 6]**
*y_values1*
**money_spent = [10, 12, 12, 10, 14, 22, 24]**
*y_values2*
**money_spent_2 = [11, 14, 15, 15, 22, 21, 12]**

*assigend to one plot*
**plt.plot(days, money_spent)**
**plt.plot(days, money_spent_2)**

plt.show()

**# Create subplots**
plt.subplot(*rows*, *columns*, *index_of_subplot*)
**# Example**
*# First Subplot*
plt.subplot(1, 2, 1)
plt.plot(x, y, color='green')

*# Second Subplot*
plt.subplot(1, 2, 2)
plt.plot(x, y, color='steelblue')

**# Format Subplots**
plt.subplots_adjust(*arguements*)
*left, right, top, bottom* **-margin**
*wspace, hspace* **horizontal/vertical margin between plots**

**# Create Legend**
plt.legend(["*first_line*", "*second_line*", *loc=*])

**# loc Numbercode**
1  upper left
2  upper right
3  lower left
4  lower right
5  right
6  center left
7  center right
8  lower center
9  upper center
10 center

**# Create Figure with custom size**
plt.figure(figsize=(*width, heigth*)) 
plt.plot(x, y)
plt.savefig('tall_and_narrow.*png/ .svg/ .pdf*')

**# Specify subplot to modify**
ax1 = plt.subplot(*row*, *column*, *index*)
**# Attributes**
ax1.set_xticks([*1, 2, 4*])
ax1.set_yticks([*0.1, 0.2, ...*])

ax1.set_xticklabels(["Jan", "Feb", "Apr"], **rotation=30**)
*# **rotation=degrees** rotates the labels* 

ax1.set_yticklabels(["10%", "20%", ...])

plt.**text**(x_coord, y_coord, "text");

plt.scatter(x_values, y_values)

**# We have to specifiy the location of each Dataset in the Plot using this pattern: ** 

n = ?  # *Number of specific dataset*
t = ? # *Number of datasets*
d = ? # *Number of sets of bars*
w = 0.8 # *Width of each bar*

x_values1 = [t\*element + w\*n **for** element **in** range(d)]

**# Get x_values in the middle of both bars**
middle_x = [ (a + b) / 2.0 for a, b in zip(x_values1, x_values2)]

**# We use the keyword *bottom* to do this**
**# The top bar will have *bottom* set as height**

*# First Bar*
video_game_hours = [1, 2, 2, 1, 2]
plt.bar(range(len(video_game_hours)),
  video_game_hours) 

*# Second Bar*
book_hours = [2, 3, 4, 2, 1]
plt.bar(range(len(book_hours)),
  book_hours,
  **bottom**=video_game_hours)

**# Get each bottom for 3+ bars**
sport_hours = np.add(video_game_hours, book_hours)

**# Use the keyword *yerr* to repersent the error range**

values = [10, 13, 11, 15, 20]
yerr = [1, 3, 0.5, 2, 4] # singe value possible
plt.bar(y, x, **yerr**=yerr, **capsize**=10)
plt.show()

x = range(3)
y = [10, 12, 13]

**y_lower** = [8, 10, 11]
**y_upper** = [i + 2 **for** i **in** y_values]

**# Calculate a % deviation**
**y_lower_bound** = [element - (element * error_in_decimal) for element in original_list_of_y_values]

**#this is the shaded error**
plt.fill_between(x, **y_lower, y_upper, alpha**=0.2)

**#this is the line itself**
plt.plot(x, y)

plt.show()

payment_names = ["Card Swipe", "Cash", "Apple Pay", "Other"]
payment_freqs = [270, 77, 32, 11]

**# Creating Pie Chart**
plt.pie(payment_freqs)
plt.axis('equal')

**# Two Methods for Labeling**
*# First Method*
plt.legend(payment_names)
*# Second Method (directly when creating)*
plt.pie(payment_freqs, labels=payment_names)

**Show percentages of total in each slice:**
plt.pie(payment_freqs, labels=payment_names, **autopct='%0.1f%%'**)
*# autopct takes a string formatting instruction*
*# %d%% -> round to decimal*

plt.show()

**# Create one Histogram**
plt.hist(dataset, range=(0,100), **bins**=20)
*# Specifiy number of **bins** (default = 10)*

**# Create multiple Histograms**
plt.hist(a, **alpha**=0.5, **normed**=True)
plt.hist(b, **histtype**='step', **linewidth**=2 **normed**=True)