Informatics Practices
How are following two codes similar or different ? What output will they produce ?
(i)
df = pd.read_csv("data.csv", nrows = 5)
print(df)
(ii)
df = pd.read_csv("data.csv")
print(df)
Answer
The two codes are similar in that they both use pd.read_csv() to read a CSV file named 'data.csv' into a pandas DataFrame df. However, they differ in their usage of the nrows parameter. The first code uses the nrows parameter with a value of 5, indicating that it reads only the first 5 rows of the CSV file. On the other hand, the second code does not have the nrows parameter, so it reads the entire CSV file.
For code (i), the output will be a DataFrame containing the first 5 rows of the 'data.csv' file. For code (ii), the output will be a DataFrame containing all the rows of the 'data.csv' file.
Related Questions
Predict the output of following code fragments one by one. For every next code fragment, consider that the changes by previous code fragment are in place. That is, for code fragment (b), changes made by code fragment (a) are persisting ; for (c), changes by (a) and (b) are persisting and so on.
(a)
import pandas as pd columns=['2015', '2016', '2017', '2018'] index=['Messi', 'Ronaldo', 'Neymar', 'Hazard'] df = pd.DataFrame(columns = columns, index = index) print(df) df.to_csv("c:\one.csv")(b)
df['2015']['Messi'] = 12 df['2016']['Ronaldo'] = 11 df['2017']['Neymar'] = 8 df['2018']['Hazard'] = 16 print(df) df.to_csv("c:\\two.csv", sep = '@')(c)
new_df = pd.read_csv('c:\one.csv', index_col = 0) print(new_df)(d)
new_df = pd.read_csv('c:\one.csv') print(new_df)(e)
new_df = pd.read_csv('c:\\two.csv') print(new_df)(f)
new_df = pd.read_csv('c:\\two.csv', sep = '@') print(new_df)Are the following two statements same ? Why/Why not ?
(i)
pd.read_csv('zoo.csv', sep = ',')(ii)
pd.read_csv('zoo.csv')Write Python statement to export the DataFrame to a CSV file named data.csv stored at D: drive.
What is the difference between following two statements ?
(i)
df.to_sql('houses', con = conn, if_exists = 'replace')(ii)
df.to_sql('houses', con = conn, if_exists = 'replace', index = False)