a Pandas DataFrame
a Pandas Series: a one-dimensional labeled array capable of holding any data type with axis labels or index. An example of a Series object is one column from a DataFrame.
a NumPy ndarray, which can be a record or structured
a two-dimensional ndarray
dictionaries of one-dimensional ndarray’s, lists, dictionaries or Series.

Note the difference between np.ndarray and np.array() . The former is an actual data type, while the latter is a function to make arrays from other data structures.

Understanding how data frames work in pandas

Indexing and selecting data

https://www.youtube.com/watch?v=OYZNk7Z9s6I

In pandas columns ad index(aka row labels) are the names for the x and y name of the data frame.

Index and columns are always there for pandas, its not something that is a option.

difference between loc, iloc, ix

Useful terms

print(df.columns)

print(df['Name'])

print(df['Name'][0:5])

print(df.Name)

print(df[['Name', 'Speed', 'HP']])

print(df.iloc[1])

print(df.iloc[1:4])

print(df.iloc[2,1])

index_list = df.index.tolist()

column_name_list = df["column_name"].tolist()

#reading headers

#read each column

#can specify if only want 0-5 etc

#can also do like df.Name

#looking at specific things

#iloc is intiger location

#can do multiple rows

#Read specific location (row, column)

#converting a df's index to list

converting a column to list

#sometimes we want to go through each row of the data

for index, row in df.iterrows(): #itter through rows

print(index, row)

write dataframe

friends = [{'name' : 'Jone', 'age' : 20, 'job': 'student'},

{'name' : 'amy', 'age' : 10, 'job': 'none'},

{'name' : 'Nate', 'age' : 30, 'job': 'teacher'}]

df = pd.DataFrame(friends)

df = df[['name', 'age', 'job']]

df. head

[{column:row}, {colums, row2},

detail example see: https://www.youtube.com/watch?v=hmYdzvmcTD8

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