<jupyter_start><jupyter_text>> Note: Always open in Colab for the best learning experience.  Pandas ChallengeHere you will apply your knowledge from the lesson to play around with pandas even more!<jupyter_code>import numpy as np
import pandas as pd

import IPython.display as ipd

def display_series(series):
    ipd.display(series)

def display_df(df):
    ipd.display(ipd.HTML(df.to_html()))<jupyter_output><empty_output><jupyter_text>Working with Pandas Series<jupyter_code>arr = np.random.randint(low=0, high=100, size = 30)
print(arr)

# TODO: Create a pandas series from the numpy array arr
s = #

# TODO: Calculate the mean, median, and standard deviation(std) for the series
# TODO: Slice array arr to get the last 4 values

# TODO: Slice the array arr to get the elements greater than its mean
# TODO: Add the number 5 to every number in array arr
add5_series = #<jupyter_output><empty_output><jupyter_text>Working with Dataframe<jupyter_code># TODO: Create a dictionary with arr and add5_series for new Dataframe
data_dict = {}

# TODO: Use data_dict to build dataframe
df = 
# TODO: View the index of df
# TODO: View columns of df
# TODO: Create a column called 'times3' where you multiply series 's' by 5
# TODO: Select two columns from df that you choose
# TODO: Select the rows where the numbers in the original s column is less than the mean of the 'times3' column
# TODO: Use .head() method to look at the first 5 rows
# TODO: Use .shape attribute to look at the number of rows and cols that df has
# TODO: Use .loc[] to select rows 10-15
# TODO: Use .loc[] to select a column and then use .nunique() and .value_counts() on that column separately
# TODO: View the distribution of one column using a histogram (.hist())
# TODO: Create a scatterplot of two columns
# TODO: Concatenate the two df's together both column-wise and row-wise separately
# TODO: Create a new column that has the number 10 in all elements
# TODO: Perfom an inner join on the last 'combined' dataframe and 'df' on a specific column<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07313505607020965, 'fraction_numerical': 0.009751340809361287, 'mean_word_length': 4.731843575418995, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 14, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '486877', 'license': 'MIT', 'n_tokens_mistral': 629, 'n_tokens_neox': 605, 'n_words': 295, 'path': 'basics/challenges/Basics_Pandas_Challenges.ipynb', 'repo_name': 'BreakoutMentors/Data-Science-and-Machine-Learning', 'size': '2051'}