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628 explicit df names #654

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Jul 24, 2023
Merged

628 explicit df names #654

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73ed6b3
rename 'df' in ep8 to 'europe'
sjvrijn Jul 6, 2023
95a52bd
rename 'df' to 'asia' when specific, and to 'countries' in function
sjvrijn Jul 6, 2023
3cc0420
prepend new dataframe names with 'data_'
sjvrijn Jul 24, 2023
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8 changes: 4 additions & 4 deletions episodes/08-data-frames.md
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,7 @@ and the Gapminder GDP data for Europe has been loaded:
```python
import pandas as pd

df = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
```

Write an expression to find the Per Capita GDP of Serbia in 2007.
Expand All @@ -345,7 +345,7 @@ Write an expression to find the Per Capita GDP of Serbia in 2007.
The selection can be done by using the labels for both the row ("Serbia") and the column ("gdpPercap\_2007"):

```python
print(df.loc['Serbia', 'gdpPercap_2007'])
print(data_europe.loc['Serbia', 'gdpPercap_2007'])
```

The output is
Expand All @@ -367,8 +367,8 @@ The output is
what rule governs what is included (or not) in numerical slices and named slices in Pandas?

```python
print(df.iloc[0:2, 0:2])
print(df.loc['Albania':'Belgium', 'gdpPercap_1952':'gdpPercap_1962'])
print(data_europe.iloc[0:2, 0:2])
print(data_europe.loc['Albania':'Belgium', 'gdpPercap_1952':'gdpPercap_1962'])
```

::::::::::::::: solution
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14 changes: 7 additions & 7 deletions episodes/16-writing-functions.md
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Expand Up @@ -458,8 +458,8 @@ Assume that the following code has been executed:
```python
import pandas as pd

df = pd.read_csv('data/gapminder_gdp_asia.csv', index_col=0)
japan = df.loc['Japan']
data_asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col=0)
japan = data_asia.loc['Japan']
```

1. Complete the statements below to obtain the average GDP for Japan
Expand All @@ -475,7 +475,7 @@ japan = df.loc['Japan']

```python
def avg_gdp_in_decade(country, continent, year):
df = pd.read_csv('data/gapminder_gdp_'+___+'.csv',delimiter=',',index_col=0)
data_countries = pd.read_csv('data/gapminder_gdp_'+___+'.csv',delimiter=',',index_col=0)
____
____
____
Expand Down Expand Up @@ -504,8 +504,8 @@ japan = df.loc['Japan']

```python
def avg_gdp_in_decade(country, continent, year):
df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = df.loc[country]
data_countries = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = data_countries.loc[country]
gdp_decade = 'gdpPercap_' + str(year // 10)
avg = (c.loc[gdp_decade + '2'] + c.loc[gdp_decade + '7'])/2
return avg
Expand All @@ -515,8 +515,8 @@ japan = df.loc['Japan']

```python
def avg_gdp_in_decade(country, continent, year):
df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = df.loc[country]
data_countries = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = data_countries.loc[country]
gdp_decade = 'gdpPercap_' + str(year // 10)
total = 0.0
num_years = 0
Expand Down
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