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Introduction to Python for Data Analysis
Introduction to Python for Data Analysis
How Much Do We Earn
You may recognize the column 'money_spent'
that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!
But firstly, recall some functions:
Group Data:
df = df[['columns which we group']] .groupby(['columns on which we group'])
Visualization:
sns.barplot(df = DataFrame, x = 'column for x-axis', y = 'column for y-axis') plt.show()
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tudo estava claro?
How Much Do We Earn
You may recognize the column 'money_spent'
that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!
But firstly, recall some functions:
Group Data:
df = df[['columns which we group']] .groupby(['columns on which we group'])
Visualization:
sns.barplot(df = DataFrame, x = 'column for x-axis', y = 'column for y-axis') plt.show()
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tudo estava claro?
How Much Do We Earn
You may recognize the column 'money_spent'
that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!
But firstly, recall some functions:
Group Data:
df = df[['columns which we group']] .groupby(['columns on which we group'])
Visualization:
sns.barplot(df = DataFrame, x = 'column for x-axis', y = 'column for y-axis') plt.show()
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.
Tudo estava claro?
You may recognize the column 'money_spent'
that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!
But firstly, recall some functions:
Group Data:
df = df[['columns which we group']] .groupby(['columns on which we group'])
Visualization:
sns.barplot(df = DataFrame, x = 'column for x-axis', y = 'column for y-axis') plt.show()
Tarefa
- Group data:
- Extract only columns
'day', 'money_spent'
from thedf
DataFrame. - Group by the column
'day'
. - Apply
.mean()
function to groupeddf
. - Apply
.reset_index()
function.
- Create a barplot:
- Use
df
as the first argument. - Use column
'day'
for x-axis. - Use the column
'money_spent'
for the y-axis.
- Output barplot.