Pan__das 2

Question 1

Row slicing

Answer 1

dict(df.iloc[-2]) #-n nth row

df.iloc[-n,:]

Question 2

column slicing

Answer 2

df.iloc[:,-n]#nth column data

Question 3

check empty df

Answer 3

if df.empty:
print(“Empty”)

or check len(df)

Question 4

set column to index

Answer 4

df.set_index(‘column name’)

Question 5

Select values in one column based on the condition from other

Answer 5

df.loc[df[‘column name’] == ‘condition’] #full row

df[‘other column name].loc[df[‘column name’] == ‘condition’]

Question 6

pandas list append

Answer 6

df.append(pd.series([the list], inex=df.columns), ignore_index=True)

Question 7

pandas datatype change

Answer 7

df = df.astype({“col name”: “datatype”})

Question 8

delete pandas column on condition

Answer 8

df = df.drop(
df [ (df.score < 50) & (df.score > 20) ].index
)

Question 9

Append rows to dataframe using series

Answer 9

# A series object with same index as dataframe
series_obj = pd.Series( ['Raju', 21, 'Bangalore', 'India'], 
                        index=df.columns )
# Add a series as a row to the dataframe  
mod_df = df.append(  series_obj,
                        ignore_index=True)

Question 10

Append rows to dataframe using loc

Answer 10

# New list for append into df
list = ["Saurabh", 23, "Delhi", "india"]

# using loc methods
df.loc[len(df)] = list

Question 11

Append rows to dataframe using iloc

Answer 11

# new list to append into df
list = ['Ujjawal', 22, 'Fathua', 'India']

# usinf iloc
df.iloc[2] = list

Question 12

how to check empty df

Answer 12

if df.empty:

print(‘DataFrame is empty!’)

Question 13

tqdm

Answer 13

What is tqdm? tqdm is a Python library that allows you to output a smart progress bar by wrapping around any iterable. A tqdm progress bar not only shows you how much time has elapsed, but also shows the estimated time remaining for the iterable.

import pandas as pd
import numpy as np
from tqdm import tqdm

# Generate a dataframe with random numbers of shape 1,000 x 1,000
df = pd.DataFrame(np.random.randint(0, 100, (100000, 1000)))
# Register `pandas.progress_apply` with `tqdm`
tqdm.pandas(desc='Processing Dataframe')
# Add 3 to each value then cube for entire dataframe
df.progress_apply(lambda x: (x+3)**3)

Question 14

list of uneven list to pandas –> at the list index level

Answer 14

import itertools, pandas
pandas.DataFrame((_ for _ in itertools.zip_longest(*ff))).T

[[[1, 2, 3, 4], [1, 2], [], [2, 2, 2]]

df = 
1,2,34
1,2,nan,nan
nan, nan, nan, nan
2,2,2,nan

Question 15

list of uneven list to pandas –> at the list of list index level

Answer 15

df = pd.DataFrame(list(res[213].values())).T
[[[1, 2, 3, 4], [1, 2], [], [2, 2, 2]]

1,1,nan,2
2,2,nan,2
3,nan,nan,2
4,nan,nan,nan