Statement of Completion#b5a037c1
Dfsd Sdf has successfully completed this project.
Intro to Pandas for Data Analysis
easy
DataFrames practice: working with English Words
Resolution
Activities
Project.ipynb
In [1]:
import pandas as pd
In [2]:
df = pd.read_csv('words.csv', index_col='Word')
In [3]:
df.head()
Out[3]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| aa | 2 | 2 |
| aah | 3 | 10 |
| aahed | 5 | 19 |
| aahing | 6 | 40 |
| aahs | 4 | 29 |
Activities¶
How many elements does this dataframe have?¶
In [5]:
df.shape
Out[5]:
(172821, 2)
In [6]:
df.info()
<class 'pandas.core.frame.DataFrame'> Index: 172821 entries, aa to zyzzyvas Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Char Count 172821 non-null int64 1 Value 172821 non-null int64 dtypes: int64(2) memory usage: 4.0+ MB
In [7]:
df.count()
Out[7]:
Char Count 172821 Value 172821 dtype: int64
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What is the value of the word microspectrophotometries?¶
In [12]:
df[df.index=='microspectrophotometries']
Out[12]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| microspectrophotometries | 24 | 317 |
In [14]:
df.loc['microspectrophotometries', 'Char Count']
Out[14]:
24
In [16]:
df.index.max()
Out[16]:
'zyzzyvas'
In [17]:
df['Value'].max()
Out[17]:
319
In [18]:
df.max()
Out[18]:
Char Count 28 Value 319 dtype: int64
In [19]:
df.describe()
Out[19]:
| Char Count | Value | |
|---|---|---|
| count | 172821.000000 | 172821.000000 |
| mean | 9.087628 | 107.754179 |
| std | 2.818285 | 39.317452 |
| min | 2.000000 | 2.000000 |
| 25% | 7.000000 | 80.000000 |
| 50% | 9.000000 | 103.000000 |
| 75% | 11.000000 | 131.000000 |
| max | 28.000000 | 319.000000 |
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In [21]:
df.loc[['enfold',
'pinfish',
'superheterodyne',
'microbrew',
'glowing']]
Out[21]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| enfold | 6 | 56 |
| pinfish | 7 | 81 |
| superheterodyne | 15 | 198 |
| microbrew | 9 | 106 |
| glowing | 7 | 87 |
What is the highest possible value of a word?¶
In [50]:
len(max(df.index, key=len))
Out[50]:
28
Which of the following words have a Char Count of 15?¶
In [20]:
df[df['Char Count']==15]
Out[20]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| absorbabilities | 15 | 143 |
| abstractionisms | 15 | 182 |
| abstractionists | 15 | 189 |
| acanthocephalan | 15 | 122 |
| acceptabilities | 15 | 134 |
| ... | ... | ... |
| worthlessnesses | 15 | 220 |
| wrongheadedness | 15 | 161 |
| xerographically | 15 | 174 |
| xeroradiography | 15 | 184 |
| zoogeographical | 15 | 158 |
3192 rows × 2 columns
What is the highest possible length of a word?¶
In [22]:
df.index.sort_values
Out[22]:
<bound method Index.sort_values of Index(['aa', 'aah', 'aahed', 'aahing', 'aahs', 'aal', 'aalii', 'aaliis',
'aals', 'aardvark',
...
'zymology', 'zymosan', 'zymosans', 'zymoses', 'zymosis', 'zymotic',
'zymurgies', 'zymurgy', 'zyzzyva', 'zyzzyvas'],
dtype='object', name='Word', length=172821)>
What is the word with the value of 319?¶
In [25]:
df[df['Value']==319]
Out[25]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| reinstitutionalizations | 23 | 319 |
What is the most common value?¶
In [30]:
df['Value'].groupby(df['Value']).count().sort_values()
Out[30]:
Value
319 1
317 1
309 1
291 1
2 1
...
92 1902
99 1907
95 1915
100 1921
93 1965
Name: Value, Length: 303, dtype: int64
What is the shortest word with value 274?¶
In [ ]:
df.mode()
In [41]:
df[df['Value']==274].sort_values(by='Char Count')
Out[41]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| overprotectivenesses | 20 | 274 |
| countercountermeasure | 21 | 274 |
| psychophysiologically | 21 | 274 |
In [40]:
df['Value'].value_counts()
Out[40]:
Value
93 1965
100 1921
95 1915
99 1907
92 1902
...
317 1
304 1
300 1
319 1
278 1
Name: count, Length: 303, dtype: int64
Create a column Ratio which represents the 'Value Ratio' of a word¶
In [ ]:
df['Ratio']=df['Value']/df['Char Count']
In [55]:
df.sort_values(by='Ratio')
Out[55]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| aa | 2 | 2 | 1.000000 |
| aba | 3 | 4 | 1.333333 |
| baa | 3 | 4 | 1.333333 |
| ab | 2 | 3 | 1.500000 |
| abba | 4 | 6 | 1.500000 |
| ... | ... | ... | ... |
| pyx | 3 | 65 | 21.666667 |
| xyst | 4 | 88 | 22.000000 |
| wry | 3 | 66 | 22.000000 |
| muzzy | 5 | 111 | 22.200000 |
| xu | 2 | 45 | 22.500000 |
172821 rows × 3 columns
What is the maximum value of Ratio?¶
In [ ]:
What word is the one with the highest Ratio?¶
In [61]:
df[(df['Char Count']==17)&(df['Value']==260)]
Out[61]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| hydroxytryptamine | 17 | 260 | 15.294118 |
How many words have a Ratio of 10?¶
In [ ]:
What is the maximum Value of all the words with a Ratio of 10?¶
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Of those words with a Value of 260, what is the lowest Char Count found?¶
In [43]:
mean_char = df['Char Count'].mean()
mean_char
Out[43]:
9.087628239623657
Based on the previous task, what word is it?¶
In [48]:
df.query("`Char Count`>@mean_char")
Out[48]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| aardwolves | 10 | 120 |
| abacterial | 10 | 72 |
| abandoners | 10 | 93 |
| abandoning | 10 | 81 |
| abandonment | 11 | 103 |
| ... | ... | ... |
| zygomorphies | 12 | 176 |
| zygomorphy | 10 | 168 |
| zygosities | 10 | 154 |
| zygospores | 10 | 165 |
| zymologies | 10 | 146 |
67582 rows × 2 columns
