Statement of Completion#8a17a928
Kamal Naidu has successfully completed this project.
Intro to Pandas for Data Analysis
easy
DataFrames practice: working with English Words
Resolution
Activities
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import pandas as pd
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df = pd.read_csv('words.csv',index_col="Word")
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df.head()
Out[14]:
| 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?¶
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df.shape
Out[7]:
(172821, 3)
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df.info
Out[8]:
<bound method DataFrame.info of Word Char Count Value 0 aa 2 2 1 aah 3 10 2 aahed 5 19 3 aahing 6 40 4 aahs 4 29 ... ... ... ... 172816 zymotic 7 111 172817 zymurgies 9 143 172818 zymurgy 7 135 172819 zyzzyva 7 151 172820 zyzzyvas 8 170 [172821 rows x 3 columns]>
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What is the value of the word microspectrophotometries?¶
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df.loc["microspectrophotometries"]
Out[17]:
Char Count 24 Value 317 Name: microspectrophotometries, dtype: int64
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What is the highest possible value of a word?¶
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df.max()
Out[18]:
Char Count 28 Value 319 dtype: int64
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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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Which of the following words have a Char Count of 7 and a Value of 87?¶
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df.loc[[
"microbrew",
"pinfish",
"enfold",
"superheterodyne"
,
"glowing"
]]
Out[26]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| microbrew | 9 | 106 |
| pinfish | 7 | 81 |
| enfold | 6 | 56 |
| superheterodyne | 15 | 198 |
| glowing | 7 | 87 |
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What is the highest possible length of a word?¶
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df.loc[df['Value']==319]
Out[36]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| reinstitutionalizations | 23 | 319 |
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dt=df.loc[['Value']==274]
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[40], line 1 ----> 1 dt=df.loc[['Value']==274] File /usr/local/lib/python3.11/site-packages/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key) 1189 maybe_callable = com.apply_if_callable(key, self.obj) 1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable) -> 1191 return self._getitem_axis(maybe_callable, axis=axis) File /usr/local/lib/python3.11/site-packages/pandas/core/indexing.py:1430, in _LocIndexer._getitem_axis(self, key, axis) 1427 return self.obj.iloc[tuple(indexer)] 1429 # fall thru to straight lookup -> 1430 self._validate_key(key, axis) 1431 return self._get_label(key, axis=axis) File /usr/local/lib/python3.11/site-packages/pandas/core/indexing.py:1239, in _LocIndexer._validate_key(self, key, axis) 1232 ax = self.obj._get_axis(axis) 1233 if isinstance(key, bool) and not ( 1234 is_bool_dtype(ax.dtype) 1235 or ax.dtype.name == "boolean" 1236 or isinstance(ax, MultiIndex) 1237 and is_bool_dtype(ax.get_level_values(0).dtype) 1238 ): -> 1239 raise KeyError( 1240 f"{key}: boolean label can not be used without a boolean index" 1241 ) 1243 if isinstance(key, slice) and ( 1244 isinstance(key.start, bool) or isinstance(key.stop, bool) 1245 ): 1246 raise TypeError(f"{key}: boolean values can not be used in a slice") KeyError: 'False: boolean label can not be used without a boolean index'
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What is the word with the value of 319?¶
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df.loc[df['Value']==319 & df['Char Count']==23]
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) /tmp/ipykernel_19/2958559696.py in ?() ----> 1 df.loc[df['Value']==319 & df['Char Count']==23] /usr/local/lib/python3.11/site-packages/pandas/core/generic.py in ?(self) 1575 @final 1576 def __nonzero__(self) -> NoReturn: -> 1577 raise ValueError( 1578 f"The truth value of a {type(self).__name__} is ambiguous. " 1579 "Use a.empty, a.bool(), a.item(), a.any() or a.all()." 1580 ) ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
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What is the most common value?¶
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df.loc[df["Value"]==274].sort_values(by="Char Count")
Out[46]:
| Char Count | Value | |
|---|---|---|
| Word | ||
| overprotectivenesses | 20 | 274 |
| countercountermeasure | 21 | 274 |
| psychophysiologically | 21 | 274 |
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What is the shortest word with value 274?¶
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Create a column Ratio which represents the 'Value Ratio' of a word¶
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What is the maximum value of Ratio?¶
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df.describe()
Out[47]:
| 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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df["Ratio"]=df["Value"]/df["Char Count"]
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df
Out[49]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| aa | 2 | 2 | 1.000000 |
| aah | 3 | 10 | 3.333333 |
| aahed | 5 | 19 | 3.800000 |
| aahing | 6 | 40 | 6.666667 |
| aahs | 4 | 29 | 7.250000 |
| ... | ... | ... | ... |
| zymotic | 7 | 111 | 15.857143 |
| zymurgies | 9 | 143 | 15.888889 |
| zymurgy | 7 | 135 | 19.285714 |
| zyzzyva | 7 | 151 | 21.571429 |
| zyzzyvas | 8 | 170 | 21.250000 |
172821 rows × 3 columns
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What word is the one with the highest Ratio?¶
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df["Ratio"].max()
Out[52]:
22.5
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df.loc[df["Ratio"]==10]
Out[57]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| aardwolf | 8 | 80 | 10.0 |
| abatements | 10 | 100 | 10.0 |
| abducts | 7 | 70 | 10.0 |
| abetment | 8 | 80 | 10.0 |
| abettals | 8 | 80 | 10.0 |
| ... | ... | ... | ... |
| ycleped | 7 | 70 | 10.0 |
| yodeled | 7 | 70 | 10.0 |
| zamia | 5 | 50 | 10.0 |
| zebecs | 6 | 60 | 10.0 |
| zwieback | 8 | 80 | 10.0 |
2604 rows × 3 columns
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df.loc[df["Ratio"]==10].sort_values(by="Value",ascending=False)
Out[63]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| electrocardiographically | 24 | 240 | 10.0 |
| electroencephalographies | 24 | 240 | 10.0 |
| electroencephalographer | 23 | 230 | 10.0 |
| phonocardiographic | 18 | 180 | 10.0 |
| inconceivabilities | 18 | 180 | 10.0 |
| ... | ... | ... | ... |
| web | 3 | 30 | 10.0 |
| bug | 3 | 30 | 10.0 |
| elm | 3 | 30 | 10.0 |
| as | 2 | 20 | 10.0 |
| oe | 2 | 20 | 10.0 |
2604 rows × 3 columns
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df.loc[df["Value"]==260].sort_values(by="Char Count")
Out[64]:
| Char Count | Value | Ratio | |
|---|---|---|---|
| Word | |||
| hydroxytryptamine | 17 | 260 | 15.294118 |
| neuropsychologists | 18 | 260 | 14.444444 |
| psychophysiologist | 18 | 260 | 14.444444 |
| revolutionarinesses | 19 | 260 | 13.684211 |
| countermobilizations | 20 | 260 | 13.000000 |
| underrepresentations | 20 | 260 | 13.000000 |
In [65]:
df["Char Count"].describe()
Out[65]:
count 172821.000000 mean 9.087628 std 2.818285 min 2.000000 25% 7.000000 50% 9.000000 75% 11.000000 max 28.000000 Name: Char Count, dtype: float64
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How many words have a Ratio of 10?¶
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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?¶
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Based on the previous task, what word is it?¶
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