<jupyter_start><jupyter_text>GOOGLE PLAY<jupyter_code># to display first 7 rows
import pandas as pd

#retrieve the csv file
data = pd.read_csv("Top-Apps-in-Google-Play.csv")


for i in range(0,7):
    df = data.iloc[i]
    # print excel
    print(df)
    print()
#first 3 columns
import pandas as pd

data = pd.read_csv("Top-Apps-in-Google-Play.csv")


columns = list(data)

#add first 3 items in columns to a new list
stuff = []
for i in range(0,3):
    stuff.append(columns[i])
print(stuff)

for j in stuff:
    col = (data[j])
    print(col)
    print()
import pandas as ps

data = ps.read_csv("Top-Apps-in-Google-Play.csv")

df=data.head(1)

df<jupyter_output><empty_output><jupyter_text>CRYPTOCURRENCY<jupyter_code># print first 7 rows
import pandas as pd

ds = pd.read_csv('BTC_DATA_V3.0.csv')

for i in range(0,7):
    df = ds.iloc[i]
    print(df)
    print()
# first 3 columns
import pandas as pd

dc = pd.read_csv('BTC_DATA_V3.0.csv')

columns = list(dc)
stuff = []
for i in range(0,3):
    stuff.append(columns[i])
print(stuff)

for j in stuff:
    col = (dc[j])
    print(col)
    print()
# row and header
import pandas as pd

ds = pd.read_csv("BTC_DATA_V3.0.csv")

df = ds.head(1)
print(df)<jupyter_output>Date    Price     Open     High      Low     Vol. Change %
0  25-Mar-22  44331.0  44013.0  45112.0  43622.0  494.09M    0.72%<jupyter_text>PROGRAMMING LANGUAGES DATASET<jupyter_code># to print first 7 rows
import pandas as pd

ds = pd.read_csv("dataset.csv")

for i in range(0,7):
    dr = ds.iloc[i]
    print(dr)
    print()
# to print first 3 columns
import pandas as pd

df = pd.read_csv("dataset.csv")
columns = list(df)

lyst = []
for i in range(0,3):
    lyst.append(columns[i])
    
for j in lyst:
    col = df[j]
    print(col)
    print()
# row and header
import pandas as pd

dg = pd.read_csv("dataset.csv")

row = dg.head(1)
print(row)<jupyter_output>id                     file_path  file_size  line_count extenstion  \
0   1  ./dataset\Markdown\000001.md      34784         572         md   

   language  
0  Markdown
{'fraction_non_alphanumeric': 0.12530472940029255, 'fraction_numerical': 0.039980497318381276, 'mean_word_length': 3.0473372781065087, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '180621', 'license': 'MIT', 'n_tokens_mistral': 882, 'n_tokens_neox': 820, 'n_words': 214, 'path': 'week_7/class_project_1.ipynb', 'repo_name': 'Dumebi35/DumebiCSC102', 'size': '2051'}