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Fix: Add encoding parameter to resolve UnicodeDecodeError #3

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Fix: Add encoding parameter to resolve UnicodeDecodeError #3

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18 changes: 8 additions & 10 deletions 04.Filtering Data/1.Filter a Dataframe Based on 1 Condition.ipynb
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Expand Up @@ -2,8 +2,8 @@
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"id": "acee6c9f",
"execution_count": null,
"id": "7e1b78c3",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -12,14 +12,12 @@
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"execution_count": null,
"id": "226ff072",
"metadata": {},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down Expand Up @@ -1089,7 +1087,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -1103,7 +1101,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.13.5"
},
"toc": {
"base_numbering": 1,
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39 changes: 20 additions & 19 deletions 04.Filtering Data/2.Creating a Conditional Column from 2 Choices np.where().ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "932b054d",
"metadata": {},
"outputs": [],
Expand All @@ -12,19 +12,19 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "af2b9d82",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"id": "1d57c715",
"metadata": {
"scrolled": true
Expand Down Expand Up @@ -141,7 +141,7 @@
"2 575.00 "
]
},
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -160,7 +160,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"id": "25857fac",
"metadata": {},
"outputs": [],
Expand All @@ -178,7 +178,7 @@
"data": {
"text/plain": [
"array(['Inexpensive', 'Inexpensive', 'Inexpensive', ..., 'Inexpensive',\n",
" 'Inexpensive', 'Inexpensive'], dtype='<U11')"
" 'Inexpensive', 'Inexpensive'], shape=(1303,), dtype='<U11')"
]
},
"execution_count": 6,
Expand Down Expand Up @@ -389,7 +389,7 @@
"Price_tier\n",
"Inexpensive 1166\n",
"Expensive 137\n",
"dtype: int64"
"Name: count, dtype: int64"
]
},
"execution_count": 9,
Expand All @@ -412,17 +412,18 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 10,
"id": "875f31cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Small', 'Small', 'Big', ..., 'Small', 'Big', 'Big'], dtype='<U5')"
"array(['Small', 'Small', 'Big', ..., 'Small', 'Big', 'Big'],\n",
" shape=(1303,), dtype='<U5')"
]
},
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -434,7 +435,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 11,
"id": "078b29c2",
"metadata": {},
"outputs": [],
Expand All @@ -445,7 +446,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 12,
"id": "c6e3cffa",
"metadata": {
"scrolled": false
Expand Down Expand Up @@ -614,7 +615,7 @@
"4 1803.60 Inexpensive Small "
]
},
"execution_count": 16,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -626,7 +627,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 13,
"id": "905ea459",
"metadata": {},
"outputs": [
Expand All @@ -636,10 +637,10 @@
"Scren_size\n",
"Big 835\n",
"Small 468\n",
"dtype: int64"
"Name: count, dtype: int64"
]
},
"execution_count": 17,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -652,7 +653,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -666,7 +667,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.13.5"
},
"toc": {
"base_numbering": 1,
Expand Down
8 changes: 4 additions & 4 deletions 04.Filtering Data/3.Filter a Dataframe Based on 2 or More Conditions.ipynb
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Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down Expand Up @@ -1665,7 +1665,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -1679,7 +1679,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.13.5"
},
"toc": {
"base_numbering": 1,
Expand Down
8 changes: 4 additions & 4 deletions 04.Filtering Data/4.Creating a Conditional Column from More Than 2 Choices np.select().ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down Expand Up @@ -936,7 +936,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -950,7 +950,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.13.5"
},
"toc": {
"base_numbering": 1,
Expand Down
4 changes: 2 additions & 2 deletions 04.Filtering Data/5.The .isin() Method.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions 04.Filtering Data/6.Find Duplicate Rows with the .duplicated() Method.ipynb
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Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions 04.Filtering Data/7.Drop Duplicate Elements with the .drop_duplicates() Method.ipynb
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Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions 04.Filtering Data/8.Get and Count Unique Values The unique() and nunique() methods.ipynb
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Expand Up @@ -12,14 +12,14 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"id": "5349f28e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion 04.Filtering Data/Exercises/1.Exercise - Filter a Dataframe Based on 1 Condition.ipynb
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Expand Up @@ -19,7 +19,7 @@
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion ...Data/Exercises/2.Exercise - Creating a Conditional Column from 2 Choices np.where().ipynb
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Expand Up @@ -20,7 +20,7 @@
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion ...ses/4.Exercise - Creating a Conditional Column from More Than 2 Choices np.select().ipynb
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Expand Up @@ -19,7 +19,7 @@
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down
12 changes: 11 additions & 1 deletion ...a/Exercises/7.Exercise - Drop Duplicate Elements with the .drop_duplicates() Method.ipynb
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Expand Up @@ -9,7 +9,17 @@
},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv')"
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "087faadb",
"metadata": {},
"outputs": [],
"source": [
"df_laptops = pd.read_csv('laptop_price.csv', encoding='latin1')"
]
},
{
Expand Down