Exploring data using Pandas¶
So far we explored Python and a few native libraries. Now we will play a little to simplify our life with tools to conduct some data analysis.
Pandas is the most popular library (so far) to import and handle data in Python.
Let's import some data from a CSV file¶
When downloading my ipynb, remember to also get the commits_pr.csv file
In [1]:
import pandas
cpr = pandas.read_csv("commits_pr.csv")
It became this easy to read a CSV file!!!
And more... Look at what my cpr is:
In [2]:
type(cpr)
Out[2]:
pandas.core.frame.DataFrame
Yes! A DataFrame. And it reads really nice, look:
In [3]:
cpr.tail()
### We can use head() and tail() functions to see a bit less
Out[3]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 42087 | user36933 | node | javascript | 14285 | 1 |
| 42088 | user36934 | react | javascript | 8762 | 2 |
| 42089 | user36934 | rails | ruby | 27508 | 1 |
| 42090 | user36935 | cocos2d-x | C++ | 15047 | 1 |
| 42091 | user36936 | node | javascript | 9508 | 2 |
Before moving forward... Explaining a little about this dataset.
This dataset represents a series of Pull Requests made to a subset of projects hosted by GitHub. We worked on this data to capture a specific type of contributor, which we called casual contributor. These contributors are known by having a single pull request accepted in a project and not coming back (i.e., they have no long-term commitment to the project).
In this specific dataset, you will find the following columns:
user: represent a user in GitHub (anonymized here)project_name: the name of GitHub project in which the pull request was acceptedprog_lang: programming language of the projectpull_req_num: unique identifier of the pull requestnum_commits: number of commits sent within that specific pull request
Some information about the dataframe¶
Dimensions/shape of the dataset (lines vs. columns)
In [4]:
cpr.shape
Out[4]:
(42092, 5)
What about the column names?
In [5]:
cpr.columns
Out[5]:
Index(['user', 'project_name', 'prog_lang', 'pull_req_number', 'num_commits'], dtype='object')
And the datatype per column?
In [6]:
cpr.dtypes
Out[6]:
user object project_name object prog_lang object pull_req_number int64 num_commits int64 dtype: object
Some more information: info() method prints information including the index dtype and column dtypes, non-null values and memory usage.
In [7]:
cpr.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 42092 entries, 0 to 42091 Data columns (total 5 columns): user 42092 non-null object project_name 42092 non-null object prog_lang 42092 non-null object pull_req_number 42092 non-null int64 num_commits 42092 non-null int64 dtypes: int64(2), object(3) memory usage: 1.6+ MB
What is the type of a specific column???
In [8]:
type(cpr["num_commits"])
Out[8]:
pandas.core.series.Series
A serie is a list, with one dimension, indexed. Each column of a dataframe is a series
Before moving ahead, we can use the types to filter some columns.
Let's say we want only the columns that store int:
In [9]:
int_columns = cpr.dtypes[cpr.dtypes == "int64"].index
int_columns
Out[9]:
Index(['pull_req_number', 'num_commits'], dtype='object')
Now... I just want to see these columns... BOOM
In [10]:
cpr[int_columns].head()
Out[10]:
| pull_req_number | num_commits | |
|---|---|---|
| 0 | 122 | 1 |
| 1 | 3325 | 1 |
| 2 | 2128 | 2 |
| 3 | 2663 | 1 |
| 4 | 7901 | 1 |
What about statistical information about my DataFrame?¶
describe() method provides a summary of numeric values in your dataset: mean, standard deviation, minimum, maximum, 1st quartile, 2nd quartile (median), 3rd quartile of the columns with numeric values. It also counts the number of variables in the dataset (are there missing variables?)
In [11]:
cpr.describe()
Out[11]:
| pull_req_number | num_commits | |
|---|---|---|
| count | 42092.000000 | 42092.000000 |
| mean | 4452.145681 | 3.824242 |
| std | 6152.304478 | 20.760123 |
| min | 1.000000 | 1.000000 |
| 25% | 628.000000 | 1.000000 |
| 50% | 2007.000000 | 1.000000 |
| 75% | 5534.250000 | 2.000000 |
| max | 38174.000000 | 385.000000 |
We can do it for a Series...
In [12]:
#cpr["num_commits"].describe()
cpr.num_commits.describe()
Out[12]:
count 42092.000000 mean 3.824242 std 20.760123 min 1.000000 25% 1.000000 50% 1.000000 75% 2.000000 max 385.000000 Name: num_commits, dtype: float64
In [13]:
#LOOK at this with a non-numeric column
cpr.prog_lang.describe() #either way work.
Out[13]:
count 42092 unique 17 top ruby freq 8147 Name: prog_lang, dtype: object
And we can get specific information per column
In [14]:
cpr.num_commits.median()
Out[14]:
1.0
In [15]:
cpr.num_commits.mean()
Out[15]:
3.8242421362729258
In [16]:
cpr.num_commits.std()
Out[16]:
20.76012335707578
In [17]:
cpr.sort_values("num_commits", ascending=False).head(10)
Out[17]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 38987 | user34165 | three.js | javascript | 7832 | 385 |
| 705 | user640 | cocos2d-x | C++ | 6866 | 364 |
| 7335 | user6426 | redis | C | 3506 | 315 |
| 19587 | user17126 | jenkins | java | 2718 | 307 |
| 35826 | user31347 | redis | C | 3230 | 290 |
| 13300 | user11672 | cocos2d-x | C++ | 16576 | 281 |
| 3601 | user3214 | three.js | javascript | 7808 | 277 |
| 13873 | user12167 | spring-framework | java | 642 | 273 |
| 26360 | user23077 | Faker | php | 660 | 259 |
| 18632 | user16293 | libgdx | java | 814 | 258 |
We can sort using many columns, by using a list (sort will happen from the first item to the last)
In [18]:
cpr.sort_values(["prog_lang", "project_name", "num_commits"], ascending=False).head(10)
Out[18]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 14351 | user12556 | winjs | typescript | 678 | 11 |
| 40943 | user35906 | winjs | typescript | 1609 | 10 |
| 35890 | user31404 | winjs | typescript | 565 | 6 |
| 1800 | user1614 | winjs | typescript | 1179 | 3 |
| 20245 | user17684 | winjs | typescript | 1559 | 3 |
| 29167 | user25562 | winjs | typescript | 30 | 3 |
| 4780 | user4214 | winjs | typescript | 44 | 2 |
| 5142 | user4533 | winjs | typescript | 185 | 2 |
| 7862 | user6897 | winjs | typescript | 1515 | 2 |
| 32077 | user28045 | winjs | typescript | 428 | 2 |
In [19]:
cpr.head(10)
Out[19]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 0 | user1 | php-src | C | 122 | 1 |
| 1 | user2 | activeadmin | ruby | 3325 | 1 |
| 2 | user3 | YouCompleteMe | python | 2128 | 2 |
| 3 | user4 | requests | python | 2663 | 1 |
| 4 | user5 | ipython | python | 7901 | 1 |
| 5 | user6 | haste-compiler | haskell | 407 | 1 |
| 6 | user7 | select2 | javascript | 1987 | 1 |
| 7 | user8 | django | python | 8608 | 3 |
| 8 | user9 | folly | C++ | 206 | 1 |
| 9 | user10 | django | python | 4745 | 2 |
If you want to keep the sorted version, you can use the parameter inplace:
In [20]:
cpr.sort_values(["prog_lang", "project_name", "num_commits"], ascending=False, inplace=True)
In [21]:
cpr.head(10)
#cpr = pandas.read_csv("commits_pr.csv") #--> to return to the original order
Out[21]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 14351 | user12556 | winjs | typescript | 678 | 11 |
| 40943 | user35906 | winjs | typescript | 1609 | 10 |
| 35890 | user31404 | winjs | typescript | 565 | 6 |
| 1800 | user1614 | winjs | typescript | 1179 | 3 |
| 20245 | user17684 | winjs | typescript | 1559 | 3 |
| 29167 | user25562 | winjs | typescript | 30 | 3 |
| 4780 | user4214 | winjs | typescript | 44 | 2 |
| 5142 | user4533 | winjs | typescript | 185 | 2 |
| 7862 | user6897 | winjs | typescript | 1515 | 2 |
| 32077 | user28045 | winjs | typescript | 428 | 2 |
Counting the occurences of variables¶
So, to count the occurrences in a column we have to select the column first, and use the method value_counts()
In [22]:
cpr.prog_lang.value_counts()
Out[22]:
ruby 8147 javascript 7052 python 4092 php 4069 C++ 2785 java 2596 C 2196 go 2103 coffeescript 2066 scala 1823 objective-c 1801 haskell 950 clojure 882 perl 663 erlang 500 typescript 343 Perl 24 Name: prog_lang, dtype: int64
But... I just want to know what are the languages out there. Is there a way?
Always
In [23]:
cpr["prog_lang"].unique()
Out[23]:
array(['typescript', 'scala', 'ruby', 'python', 'php', 'perl',
'objective-c', 'javascript', 'java', 'haskell', 'go', 'erlang',
'coffeescript', 'clojure', 'Perl', 'C++', 'C'], dtype=object)
OK! Let's do something else... Like, selecting columns and filtering data¶
Let's say that I just want to look at the columns programming language, project name and number of commits.
I can select them and create a new DF
In [24]:
selected_columns = ["prog_lang", "project_name", "num_commits"]
my_subset = cpr[selected_columns]
my_subset.head()
Out[24]:
| prog_lang | project_name | num_commits | |
|---|---|---|---|
| 14351 | typescript | winjs | 11 |
| 40943 | typescript | winjs | 10 |
| 35890 | typescript | winjs | 6 |
| 1800 | typescript | winjs | 3 |
| 20245 | typescript | winjs | 3 |
What if now I want to filter those projects written in C language?
In [25]:
only_C = cpr[(cpr["prog_lang"]=='C') & (cpr["num_commits"]==2)]
only_C.describe()
Out[25]:
| pull_req_number | num_commits | |
|---|---|---|
| count | 389.000000 | 389.0 |
| mean | 3815.380463 | 2.0 |
| std | 3264.957089 | 0.0 |
| min | 3.000000 | 2.0 |
| 25% | 1061.000000 | 2.0 |
| 50% | 2860.000000 | 2.0 |
| 75% | 5831.000000 | 2.0 |
| max | 12724.000000 | 2.0 |
We can filter whatever we want:
In [26]:
single_commit = cpr[cpr["num_commits"] == 1]
We can create filters in variables, and use whenever we want, as well
In [27]:
one_commit = cpr["num_commits"]==1
language_C = cpr["prog_lang"]=="C"
multi_commit = cpr["num_commits"]>1
In [28]:
cpr[one_commit & language_C].head(10)
Out[28]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 1625 | user1464 | twemproxy | C | 284 | 1 |
| 1696 | user1526 | twemproxy | C | 224 | 1 |
| 2259 | user2025 | twemproxy | C | 398 | 1 |
| 2522 | user2268 | twemproxy | C | 387 | 1 |
| 3210 | user2872 | twemproxy | C | 311 | 1 |
| 3946 | user3515 | twemproxy | C | 366 | 1 |
| 4774 | user4209 | twemproxy | C | 291 | 1 |
| 5802 | user5103 | twemproxy | C | 3 | 1 |
| 7326 | user6419 | twemproxy | C | 58 | 1 |
| 7811 | user6850 | twemproxy | C | 217 | 1 |
And... we can use OR (|) and AND(&) to play!
In [29]:
cpr[one_commit & language_C].head(10)
Out[29]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 1625 | user1464 | twemproxy | C | 284 | 1 |
| 1696 | user1526 | twemproxy | C | 224 | 1 |
| 2259 | user2025 | twemproxy | C | 398 | 1 |
| 2522 | user2268 | twemproxy | C | 387 | 1 |
| 3210 | user2872 | twemproxy | C | 311 | 1 |
| 3946 | user3515 | twemproxy | C | 366 | 1 |
| 4774 | user4209 | twemproxy | C | 291 | 1 |
| 5802 | user5103 | twemproxy | C | 3 | 1 |
| 7326 | user6419 | twemproxy | C | 58 | 1 |
| 7811 | user6850 | twemproxy | C | 217 | 1 |
What if we want the pull requests with more than one commit for the projects written in "C" and those with 2 commits for the projects written in "typescript"???¶
Let's do it!
In [30]:
#####
two_commits = cpr["num_commits"]==2
language_typescript = cpr["prog_lang"]=="typescript"
cpr[(one_commit & language_C) | (two_commits & language_typescript)]
Out[30]:
| user | project_name | prog_lang | pull_req_number | num_commits | |
|---|---|---|---|---|---|
| 4780 | user4214 | winjs | typescript | 44 | 2 |
| 5142 | user4533 | winjs | typescript | 185 | 2 |
| 7862 | user6897 | winjs | typescript | 1515 | 2 |
| 32077 | user28045 | winjs | typescript | 428 | 2 |
| 14874 | user13017 | typescript-node-definitions | typescript | 10 | 2 |
| 4628 | user4086 | tsd | typescript | 251 | 2 |
| 6163 | user5410 | tsd | typescript | 99 | 2 |
| 19480 | user17042 | tsd | typescript | 227 | 2 |
| 28014 | user24539 | tsd | typescript | 223 | 2 |
| 2938 | user2628 | shumway | typescript | 1660 | 2 |
| 29652 | user25959 | shumway | typescript | 7 | 2 |
| 35752 | user31281 | shumway | typescript | 2156 | 2 |
| 39930 | user35002 | shumway | typescript | 119 | 2 |
| 12063 | user10592 | reddcoin | typescript | 8 | 2 |
| 24217 | user21197 | reddcoin | typescript | 80 | 2 |
| 18678 | user16336 | primecoin | typescript | 15 | 2 |
| 37326 | user32674 | primecoin | typescript | 4 | 2 |
| 3309 | user2954 | litecoin | typescript | 373 | 2 |
| 8441 | user7412 | litecoin | typescript | 356 | 2 |
| 12193 | user10705 | litecoin | typescript | 3 | 2 |
| 14382 | user12584 | litecoin | typescript | 16 | 2 |
| 14478 | user12666 | litecoin | typescript | 2 | 2 |
| 34893 | user30521 | litecoin | typescript | 124 | 2 |
| 41348 | user36268 | litecoin | typescript | 242 | 2 |
| 18750 | user16399 | egret-core | typescript | 125 | 2 |
| 15621 | user13681 | doppio | typescript | 415 | 2 |
| 29010 | user25413 | doppio | typescript | 417 | 2 |
| 41824 | user36687 | doppio | typescript | 387 | 2 |
| 430 | user398 | TypeScript | typescript | 8394 | 2 |
| 2542 | user2286 | TypeScript | typescript | 13045 | 2 |
| ... | ... | ... | ... | ... | ... |
| 25269 | user22129 | cphalcon | C | 11147 | 1 |
| 26031 | user22791 | cphalcon | C | 11144 | 1 |
| 26174 | user22918 | cphalcon | C | 3192 | 1 |
| 26259 | user22997 | cphalcon | C | 12609 | 1 |
| 26272 | user23006 | cphalcon | C | 2455 | 1 |
| 26445 | user23156 | cphalcon | C | 11214 | 1 |
| 27626 | user24204 | cphalcon | C | 11951 | 1 |
| 27992 | user24520 | cphalcon | C | 2176 | 1 |
| 28734 | user25172 | cphalcon | C | 636 | 1 |
| 29569 | user25888 | cphalcon | C | 10007 | 1 |
| 29871 | user26145 | cphalcon | C | 10802 | 1 |
| 30035 | user26276 | cphalcon | C | 3051 | 1 |
| 30057 | user26297 | cphalcon | C | 12757 | 1 |
| 30508 | user26700 | cphalcon | C | 9927 | 1 |
| 30829 | user26967 | cphalcon | C | 9918 | 1 |
| 32633 | user28536 | cphalcon | C | 1033 | 1 |
| 32940 | user28804 | cphalcon | C | 11983 | 1 |
| 33441 | user29243 | cphalcon | C | 1490 | 1 |
| 33795 | user29560 | cphalcon | C | 2354 | 1 |
| 37159 | user32521 | cphalcon | C | 3340 | 1 |
| 39986 | user35049 | cphalcon | C | 2689 | 1 |
| 41069 | user36019 | cphalcon | C | 11071 | 1 |
| 41416 | user36327 | cphalcon | C | 11148 | 1 |
| 41484 | user36386 | cphalcon | C | 34 | 1 |
| 41679 | user36551 | cphalcon | C | 3082 | 1 |
| 2465 | user2213 | ccv | C | 83 | 1 |
| 14883 | user13025 | ccv | C | 4 | 1 |
| 21334 | user18630 | ccv | C | 19 | 1 |
| 21728 | user18978 | ccv | C | 132 | 1 |
| 39827 | user34908 | ccv | C | 150 | 1 |
1367 rows × 5 columns
What if I wanted to convert number of commits into a feature by creating bands of values that we define:
- 1 commit = group 1
- 2 - 5 commits = group 2
- 6 - 20 commits = group 3
- more than 20 = group 4
In [31]:
cpr.loc[cpr["num_commits"]==1, "group_commit"]=1
cpr.loc[(cpr["num_commits"]>1) & (cpr["num_commits"]<=5), "group_commit"]=2
cpr.loc[(cpr["num_commits"]>5) & (cpr["num_commits"]<=20), "group_commit"]=3
cpr.loc[cpr["num_commits"]>20, "group_commit"]=4
cpr.group_commit = cpr.group_commit.astype('int32')
In [32]:
cpr.head()
Out[32]:
| user | project_name | prog_lang | pull_req_number | num_commits | group_commit | |
|---|---|---|---|---|---|---|
| 14351 | user12556 | winjs | typescript | 678 | 11 | 3 |
| 40943 | user35906 | winjs | typescript | 1609 | 10 | 3 |
| 35890 | user31404 | winjs | typescript | 565 | 6 | 3 |
| 1800 | user1614 | winjs | typescript | 1179 | 3 | 2 |
| 20245 | user17684 | winjs | typescript | 1559 | 3 | 2 |
I challenge you:¶
What if: I wanted to know how the average of num_commits for those pull requests in group_commit 4???
In [ ]:
I challenge you (2):¶
Can you do that average per language?
In [33]:
cpr[cpr["prog_lang"] == "typescript"].quantile(0.75)
Out[33]:
pull_req_number 8213.5 num_commits 2.0 group_commit 2.0 Name: 0.75, dtype: float64
Some more...¶
Let's work with a new dataset...
This is not only related to casual contributors, but all contributors
In [34]:
commits_complete = pandas.read_csv('commit_complete.csv')
commits_complete.sort_values('num_commits', ascending=False).head(10)
Out[34]:
| user | project_name | prog_lang | num_commits | additions | deletions | files_changed | num_comments | |
|---|---|---|---|---|---|---|---|---|
| 52041 | user_13168 | cocos2d-x | C++ | 10000.0 | 1320472.0 | 24870.0 | 5865.0 | 0.0 |
| 52036 | user_13168 | cocos2d-x | C++ | 10000.0 | 1321513.0 | 24870.0 | 5905.0 | 0.0 |
| 54883 | user_13227 | cocos2d-x | C++ | 10000.0 | 1549480.0 | 843841.0 | 9726.0 | 0.0 |
| 52033 | user_13168 | cocos2d-x | C++ | 10000.0 | 1320976.0 | 24870.0 | 5892.0 | 0.0 |
| 61760 | user_13751 | cocos2d-x | C++ | 10000.0 | 1163795.0 | 24870.0 | 5241.0 | 0.0 |
| 52034 | user_13168 | cocos2d-x | C++ | 10000.0 | 1321296.0 | 24870.0 | 5905.0 | 0.0 |
| 61813 | user_13757 | cocos2d-x | C++ | 10000.0 | 1324952.0 | 24870.0 | 5903.0 | 0.0 |
| 52035 | user_13168 | cocos2d-x | C++ | 10000.0 | 1321419.0 | 24870.0 | 5890.0 | 0.0 |
| 61841 | user_13764 | cocos2d-x | C++ | 10000.0 | 1539898.0 | 21568.0 | 6574.0 | 0.0 |
| 54495 | user_13188 | cocos2d-x | C++ | 10000.0 | 1249461.0 | 1129333.0 | 8848.0 | 0.0 |
In [35]:
commits_complete['num_commits'].corr(commits_complete['additions'])
Out[35]:
0.6573205139433453
In [36]:
commits_complete.corr()
Out[36]:
| num_commits | additions | deletions | files_changed | num_comments | |
|---|---|---|---|---|---|
| num_commits | 1.000000 | 0.657321 | 0.151074 | 0.605152 | -0.007297 |
| additions | 0.657321 | 1.000000 | 0.244859 | 0.749543 | -0.002682 |
| deletions | 0.151074 | 0.244859 | 1.000000 | 0.566905 | 0.011876 |
| files_changed | 0.605152 | 0.749543 | 0.566905 | 1.000000 | 0.003657 |
| num_comments | -0.007297 | -0.002682 | 0.011876 | 0.003657 | 1.000000 |
In [37]:
commits_complete.corr(method='pearson').style.background_gradient(cmap='coolwarm')
Out[37]:
| num_commits | additions | deletions | files_changed | num_comments | |
|---|---|---|---|---|---|
| num_commits | 1 | 0.657321 | 0.151074 | 0.605152 | -0.00729652 |
| additions | 0.657321 | 1 | 0.244859 | 0.749543 | -0.00268151 |
| deletions | 0.151074 | 0.244859 | 1 | 0.566905 | 0.0118759 |
| files_changed | 0.605152 | 0.749543 | 0.566905 | 1 | 0.00365692 |
| num_comments | -0.00729652 | -0.00268151 | 0.0118759 | 0.00365692 | 1 |
Can we play with graphics?¶
Plot types:
- 'line' : line plot (default)
- 'bar' : vertical bar plot
- 'barh' : horizontal bar plot
- 'hist' : histogram
- 'box' : boxplot
- 'kde' : Kernel Density Estimation plot
- 'density' : same as 'kde'
- 'area' : area plot
- 'pie' : pie plot
- 'scatter' : scatter plot
- 'hexbin' : hexbin plot
Histogram
In [38]:
cpr.num_commits.plot.hist(bins=200)
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x116703e48>
In [39]:
cpr[cpr["prog_lang"]=="C"].num_commits.plot.hist(bins=20, color="red", alpha=0.5)
cpr[cpr["prog_lang"]=="java"].num_commits.plot.hist(bins=20, alpha=0.5).legend(["C", "Java"])
Out[39]:
<matplotlib.legend.Legend at 0x113399278>
In [40]:
cpr['prog_lang'].value_counts().plot.bar()
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x1134acbe0>
In [41]:
cpr[cpr["prog_lang"]== "C"].project_name.value_counts().plot.bar()
Out[41]:
<matplotlib.axes._subplots.AxesSubplot at 0x1135ca390>
In [42]:
commits_complete.plot.scatter(x = "files_changed", y = "num_commits")
Out[42]:
<matplotlib.axes._subplots.AxesSubplot at 0x11347bcf8>
In [43]:
lang_c = cpr.prog_lang=="C"
lang_java = cpr.prog_lang=="java"
lang_php = cpr.prog_lang=="php"
cpr[(lang_c) | (lang_java) | (lang_php)].boxplot(by='prog_lang', column=['num_commits'])
Out[43]:
<matplotlib.axes._subplots.AxesSubplot at 0x113489400>
In [44]:
plot = cpr[(lang_c) | (lang_java) | (lang_php)].boxplot(by='prog_lang', column=['num_commits'], showfliers=False, grid=False)
plot.set_xlabel("Language")
plot.set_ylabel("# of commits")
plot.set_title("")
Out[44]:
Text(0.5, 1.0, '')
Just to show...
that it is possible to do statistical analysis
In [45]:
from scipy import stats
stats.mannwhitneyu(cpr[(lang_c)].num_commits, cpr[(lang_java)].num_commits)
Out[45]:
MannwhitneyuResult(statistic=2481768.0, pvalue=2.1763470665307134e-20)
Exporting¶
In [46]:
my_subset.to_dict()
Out[46]:
{'prog_lang': {14351: 'typescript',
40943: 'typescript',
35890: 'typescript',
1800: 'typescript',
20245: 'typescript',
29167: 'typescript',
4780: 'typescript',
5142: 'typescript',
7862: 'typescript',
32077: 'typescript',
535: 'typescript',
2368: 'typescript',
3644: 'typescript',
6174: 'typescript',
9288: 'typescript',
9851: 'typescript',
14019: 'typescript',
17979: 'typescript',
20726: 'typescript',
25046: 'typescript',
28071: 'typescript',
28507: 'typescript',
29869: 'typescript',
34060: 'typescript',
34065: 'typescript',
39071: 'typescript',
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4935: 8,
6758: 8,
21648: 8,
15014: 7,
31411: 7,
3049: 5,
24686: 5,
29783: 5,
30300: 5,
2272: 4,
18229: 4,
20270: 4,
26081: 4,
26919: 4,
27129: 4,
...}}
In [47]:
cpr.to_csv('test.csv', sep=',')
In [ ]: