BASICS
Load a CSV File and Display Basic Information
Explore the concepts and examples below to master this topic.
import pandas as pd
df = pd.read_csv("data.csv")
print(df.info())
print(df.head())
Filter DataFrame Based on Condition
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df_filtered = df[df['column_name'] > 50]
print(df_filtered)
INTERMEDIATE
Perform GroupBy and Aggregation
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df_grouped = df.groupby('category_column').agg({'value_column': 'mean'})
print(df_grouped)
Merge Two DataFrames Based on a Common Column
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df1 = pd.read_csv("data1.csv")
df2 = pd.read_csv("data2.csv")
df_merged = pd.merge(df1, df2, on='common_column', how='inner')
print(df_merged)
ADVANCED
Apply Custom Function to DataFrame Column
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def custom_function(x):
return x * 2
df['new_column'] = df['existing_column'].apply(custom_function)
print(df.head())
Perform Time Series Analysis
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df['date_column'] = pd.to_datetime(df['date_column'])
df.set_index('date_column', inplace=True)
df_resampled = df.resample('M').mean()
print(df_resampled)
Use the reference cards below for fast lookup. Perfect for brushing up on syntax while you cook your code. 🍳
import pandas as pd
df = pd.read_csv("data.csv")
print(df.info())
print(df.head())
df_filtered = df[df['column_name'] > 50]
print(df_filtered)
df_grouped = df.groupby('category_column').agg({'value_column': 'mean'})
print(df_grouped)
df1 = pd.read_csv("data1.csv")
df2 = pd.read_csv("data2.csv")
df_merged = pd.merge(df1, df2, on='common_column', how='inner')
print(df_merged)
def custom_function(x):
return x * 2
df['new_column'] = df['existing_column'].apply(custom_function)
print(df.head())
df['date_column'] = pd.to_datetime(df['date_column'])
df.set_index('date_column', inplace=True)
df_resampled = df.resample('M').mean()
print(df_resampled)
Recipe Complete! 🎉
You've explored the full Data-Analysis recipe.
Keep practising — a language a day keeps AI away! 🤖
