<jupyter_start><jupyter_text>Reading DataReading Objects.csv and showing first 5 Rows with all Collumns<jupyter_code># Read Dataset
objects = pd.read_csv('./startup-investments/objects.csv')
pd.set_option('display.max_columns', None)
objects.head()<jupyter_output>/tmp/ipykernel_39285/22073810.py:2: DtypeWarning: Columns (3,7,9,10,17,18,21,22,23,25,26,29,30,37) have mixed types. Specify dtype option on import or set low_memory=False.
  objects = pd.read_csv('./startup-investments/objects.csv')<jupyter_text>Selecting DataSelect some interesting Rows to shrink DatasetAlso select only the Companies<jupyter_code># select interesting columns
df_objects = objects[['name','entity_type','category_code','status', 'founded_at', 'funding_total_usd']]

# only select Companies (get rid of people or other entities)
df_companies = df_objects.loc[df_objects['entity_type'] == 'Company']
df_companies = df_companies.drop('entity_type', axis=1)

df_companies = df_companies.dropna()
df_companies.head(20)<jupyter_output><empty_output><jupyter_text>Status of all Startups<jupyter_code>status=df_companies.status.value_counts()
plt.figure()

cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
plt.pie(status.values, radius=2, colors=outer_colors,labels=status.index,autopct='%1.1f%%',
       wedgeprops=dict(width=0.5, edgecolor='w'))
plt.title('Status of Start-Ups',pad=100)
plt.rcParams['figure.facecolor'] = 'white'
plt.show()<jupyter_output><empty_output><jupyter_text>Wordcloud of Startup Categories<jupyter_code>import wordcloud

w=wordcloud.WordCloud(background_color='White',scale=32)

categ = ' '.join(df_companies.category_code)

fig = plt.figure(figsize=(12, 8))
w.generate(categ)
plt.axis('off')
plt.imshow(w)<jupyter_output><empty_output><jupyter_text>total Fundings in USD<jupyter_code>fundings = df_companies[['name','funding_total_usd']]

plt.figure(figsize=(12,8))
sns.scatterplot(data=fundings,x='name',y='funding_total_usd',size='funding_total_usd')
plt.xticks([])
plt.title('Funding total in USD')
plt.xlabel("")
plt.ylabel("Funding in USD")
plt.show()

# plot takes 1minute to show :D<jupyter_output><empty_output><jupyter_text>Fundings over Time<jupyter_code>df_companies.founded_at = pd.to_datetime(df_companies.founded_at)
df_companies = df_companies.sort_values(by=['founded_at'])

plt.plot(df_companies.founded_at, df_companies.funding_total_usd)
plt.gcf().autofmt_xdate()
plt.show()<jupyter_output>/usr/lib/python3/dist-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  ndim = x[:, None].ndim
/usr/lib/python3/dist-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  x = x[:, np.newaxis]
/usr/lib/python3/dist-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  y = y[:, np.newaxis]<jupyter_text>Top 50 highest Fundings<jupyter_code># take top 50 fundings and check what companies have in common


df_funds = df_companies.sort_values(by=['funding_total_usd'], ascending=False)

df_topfunds = df_funds.head(100)
df_topfunds.head(10)


import wordcloud

w=wordcloud.WordCloud(background_color='White',scale=32)

categ = ' '.join(df_topfunds.category_code)

fig = plt.figure(figsize=(12, 8))
w.generate(categ)
plt.axis('off')
plt.imshow(w)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13605808012906695, 'fraction_numerical': 0.024468943264318366, 'mean_word_length': 7.551724137931035, 'pattern_counts': {'":': 0, '<': 27, '<?xml version=': 0, '>': 27, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '400399', 'license': 'MIT', 'n_tokens_mistral': 1438, 'n_tokens_neox': 1332, 'n_words': 254, 'path': 'startup-analysis/explore_objects.ipynb', 'repo_name': 'schmuecker/statistics-course', 'size': '3719'}