#🔒 i'm trying to analyze why there was a jump in tortilla prices for mom and pop stores from 2019-202

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small nacelle
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my idea was to take the data of the tortilla price per kilogram and compare that to the price of the other goods from both dataframes. i have both of the .csvs here.

outer joltBOT
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@small nacelle

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small nacelle
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i was able to get a year column from the df2

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(the second csv)

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i just don't understand how i'm supposed to combine them?

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a merge would work

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but is a merge what would be most useful?

slim yacht
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So what I would do is make a new dataframe with the data you care about from dataframe1. And then do the same for dataframe2. And then you can concat the two new dataframes as they will have the same columns filled with the data you care about.

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brb, hiking. 🙂

small nacelle
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hmmmm

small nacelle
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what's wrong with just concatenating it all together since i don't know what i'm looking for quite yet?

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is it bc it's super computationally intensive?

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i don't quite understand

small nacelle
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this is the plot that triggered my curiosity

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i want to compare the price of the tortilla to other goods

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for the years 2019 to 2022

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even all the way back since 2007 is fine

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but the main focus is 2019-2022

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i'm stuck as hell

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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import matplotlib.ticker as mticker
df = pd.read_csv("/Users/rahuldas/Desktop/Tortilla Dataset/tortilla_prices.csv")

print(df.head)
print(df.info()) 
print(df.shape)
print(df.columns)
print(df.dtypes)
print("hello world")
price_per_kilogram_missing = df["Price per kilogram"].isna().sum()
print(price_per_kilogram_missing)


price_per_kilogram_missing_mean = df["Price per kilogram"].mean()
print(price_per_kilogram_missing_mean)
df["Price per kilogram"] = df["Price per kilogram"].fillna(price_per_kilogram_missing_mean)
print(df["Price per kilogram"].isna().sum())
sns.set_style("whitegrid")
sns.kdeplot(data=df, x="Price per kilogram", hue="Store type", fill=True)
#plt.show()
fig, ax = plt.subplots(figsize=(6, 6))
# drawing the plot
sns.boxplot(data=df, x = "Store type", y = "Price per kilogram", color = "lightblue", ax=ax);
plt.xticks(rotation=90)
sns.despine(left=True, right=True, top=True, bottom=True)
plt.show()
ax = sns.lineplot(x = "Year", y = "Price per kilogram", data = df, hue = "Store type");
ax.xaxis.set_major_locator(mticker.MultipleLocator(3))
plt.show()
df2 = pd.read_csv("/Users/rahuldas/Desktop/Tortilla Dataset/wfp_food_prices_mex.csv", skiprows=[1])
print(df2.head)
print(df2.info())
df2["date"] = pd.to_datetime(df2["date"])
print(df2.info())
df2['Year'] = pd.DatetimeIndex(df2['date']).year
print(df2.columns)

print(df.head())
print(df2.head()) 
df_combined = pd.merge(df, df2, on="Year", how="outer")
print(df_combined.head())
print(df_combined.info())
``` @zealous galleon , is this along what you had in mind?
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this literally crashes my mac

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i'm looking at this vid

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it still gives only 5 rows and 21 columns

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!close

outer joltBOT
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