import pandas as pd
import numpy as np
import random # to generate random values
# Create a dictionary using a comprehension
d = {x:[round(random.uniform(0,1), 2) for x in range(20)] for x in range(21)}
# Create a pandas dataframe
df_dict = pd.DataFrame(d)
# How many rows and columns?
print("Total Rows: {}\nTotal Columns: {}".format(df_dict.shape[0], df_dict.shape[1]))
# Look at the first 5 rows
df_dict.head()
Total Rows: 20 Total Columns: 21
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.75 | 0.92 | 0.89 | 0.66 | 0.49 | 0.78 | 0.72 | 0.50 | 0.01 | 0.57 | ... | 0.27 | 0.49 | 0.66 | 0.82 | 0.04 | 0.95 | 0.90 | 0.31 | 0.60 | 0.00 |
1 | 0.52 | 1.00 | 0.54 | 0.76 | 0.20 | 0.04 | 0.65 | 0.21 | 0.07 | 0.47 | ... | 0.95 | 0.07 | 0.65 | 0.14 | 0.48 | 0.69 | 0.63 | 0.06 | 0.12 | 0.43 |
2 | 0.61 | 0.18 | 0.66 | 0.07 | 0.32 | 0.63 | 0.79 | 0.68 | 0.91 | 0.46 | ... | 0.27 | 0.34 | 0.29 | 0.37 | 0.74 | 0.82 | 0.08 | 0.52 | 0.60 | 0.79 |
3 | 0.69 | 0.35 | 0.91 | 0.98 | 0.02 | 0.83 | 0.66 | 0.12 | 0.61 | 0.93 | ... | 0.34 | 0.08 | 0.25 | 0.27 | 0.07 | 0.11 | 0.41 | 0.07 | 0.37 | 0.33 |
4 | 0.14 | 0.09 | 0.46 | 0.32 | 0.03 | 0.36 | 0.63 | 0.88 | 0.04 | 0.25 | ... | 0.67 | 0.22 | 0.75 | 0.79 | 0.44 | 0.06 | 0.29 | 0.66 | 0.47 | 0.27 |
5 rows × 21 columns
# Create a lists of lists using a comprehension
ls = [[round(random.randint(0,1), 2) for x in range(21)] for x in range(61)]
# Create pandas dataframe
df_ls = pd.DataFrame(ls)
# How many rows and columns?
print(f"Total Rows: %s\nTotal Columns: %s" % (df_ls.shape[0], df_ls.shape[1]))
# Look at last 5 rows
df_ls.tail()
Total Rows: 61 Total Columns: 21
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
56 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
57 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
58 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
59 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
60 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |
5 rows × 21 columns
# Create an array
arr = np.ndarray((20,21), dtype=int)
# Create a pandas dataframe
df_arr = pd.DataFrame(arr)
# How many rows and columns?
print(f"Total Rows: {df_arr.shape[0]}\nTotal Columns: {df_arr.shape[1]}")
# Look at every other row
df_arr[::2]
Total Rows: 20 Total Columns: 21
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 140280022526903 | 0 | 140280022526903 | 140280022526880 | -4294967285 | 0 | 8 | 1080615416646074368 | 0 | ... | 140280005221416 | 140279957442792 | 2 | 0 | -1 | 0 | 140277926854657 | 72 | 11 | 140280005221416 |
2 | -1 | 0 | 140277926854657 | 288 | 11 | 140280005221417 | 140279957445340 | 0 | 20 | -1 | ... | 140277926854657 | 360 | 7 | 140280005221417 | 140279957445408 | 0 | 0 | 0 | 34359738369 | 100227356819456 |
4 | 0 | 0 | 0 | 4294967296 | 1 | 140280005221416 | 0 | 432 | 2 | 140280005221417 | ... | 0 | 26 | -1 | 0 | 1 | 728 | 9 | 140280005221418 | 140279957445408 | 1 |
6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 140280005221416 | ... | 800 | 2 | 140280005221417 | 140279957445356 | -1 | 26 | -1 | 140280022435200 | 140277926854657 | 1096 |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 140280022628592 |
10 | -6202693434030450875 | 140279903539248 | 140279903539584 | -8912638606442719106 | 140279903535344 | 140279903539664 | 6089276150623630416 | 140279903535408 | 140279903539744 | -6901560858922915569 | ... | 140279903539824 | 4221092797560673003 | 140279903535792 | 140279903539904 | -6734156190334203604 | 140279903535856 | 140279903539984 | 2892974746413995488 | 140279903535920 | 140279903540064 |
12 | 1498712130516418560 | 140279903548720 | 140279903541024 | 6396686765192600008 | 140279959053824 | 140279903541064 | 0 | 140279903548855 | 140279903548864 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
14 | 1181070720269534810 | 112590557911712144 | 5 | 1325185909890810981 | 112611310928134556 | -4009890855991896988 | 1541358691887088016 | -3937833257359113840 | 112590099608572005 | 1757531472595261284 | ... | 5439844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 183 | 140279958870016 | 60 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
10 rows × 21 columns
# Create a Series
s = pd.Series([random.randint(-10,0) for x in range(21)])
# Create a pandas dataframe
df_series = pd.DataFrame([s]*20)
# How many rows and columns?
print(f"Total Rows: {df_series.shape[0]}","\nTotal Columns: {}".format(df_series.shape[1]))
# Look at a random sample of 5 rows
df_series.sample(5)
Total Rows: 20 Total Columns: 21
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
16 | -2 | -6 | -1 | -9 | 0 | -5 | -3 | -4 | -4 | -2 | ... | -2 | -9 | -3 | -4 | -7 | -6 | -4 | -9 | -8 | -5 |
8 | -2 | -6 | -1 | -9 | 0 | -5 | -3 | -4 | -4 | -2 | ... | -2 | -9 | -3 | -4 | -7 | -6 | -4 | -9 | -8 | -5 |
13 | -2 | -6 | -1 | -9 | 0 | -5 | -3 | -4 | -4 | -2 | ... | -2 | -9 | -3 | -4 | -7 | -6 | -4 | -9 | -8 | -5 |
0 | -2 | -6 | -1 | -9 | 0 | -5 | -3 | -4 | -4 | -2 | ... | -2 | -9 | -3 | -4 | -7 | -6 | -4 | -9 | -8 | -5 |
11 | -2 | -6 | -1 | -9 | 0 | -5 | -3 | -4 | -4 | -2 | ... | -2 | -9 | -3 | -4 | -7 | -6 | -4 | -9 | -8 | -5 |
5 rows × 21 columns
# Before, with row and column truncation
df_ls
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | ... | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
2 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 |
3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
4 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | ... | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
56 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
57 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
58 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
59 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
60 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |
61 rows × 21 columns
# Viewing max rows and columns displayed limit
row = pd.options.display.max_rows
# Increase amount of rows seen
col = pd.options.display.max_columns
print(f"Before:\nMax Displayed Rows: {row}\nMax Displayed Columns: {col}\n")
# Changing the max displayed rows and columns
pd.options.display.max_rows = 100
pd.options.display.max_columns = 100
row = pd.options.display.max_rows
col = pd.options.display.max_columns
print("After:\nMax Displayed Rows: %s\nMax Displayed Columns: %s" % (row, col))
Before: Max Displayed Rows: 60 Max Displayed Columns: 20 After: Max Displayed Rows: 100 Max Displayed Columns: 100
# Without truncation
df_ls
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
2 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 |
3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
4 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
5 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
6 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
7 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 |
8 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
9 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 |
10 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
11 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
12 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
13 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 |
14 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 |
15 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 |
16 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
17 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
18 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 |
19 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
20 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
21 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
22 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 |
23 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 |
24 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 |
25 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 |
26 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 |
27 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
28 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 |
29 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
30 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
31 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 |
32 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
33 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
34 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
35 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
36 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 |
37 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
38 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 |
39 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
40 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
41 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 |
42 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
43 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
44 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
45 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 |
46 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 |
47 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 |
48 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 |
49 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
50 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 |
51 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
52 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 |
53 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 |
54 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
55 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 |
56 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
57 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
58 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
59 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
60 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |