Wrong x-values in pyplot of pandas dataframe after converting indices to integers. How can I get the correct values?

smcintyre247 :

SOLVED: I have a pandas dataframe (df) that contains data on number of Haitians who immigrated to Canada from 1980 to 2013 with the indices as the years, so

>>>len(df)
34
>>>df.index
Index(['1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988',
   '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',
   '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006',
   '2007', '2008', '2009', '2010', '2011', '2012', '2013'],
  dtype='object')

I want to convert the index to integers to make plotting easier, so I wrote

>>>df.index = df.index.map(int)
Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
        1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
        2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
        2013],
       dtype='int64')

The conversion seemed to work fine, but when I try to plot my data the x-values are wrong.

>>>df.plot()
...plt.title("Immigration from Haiti")
...plt.xlabel("Year")
...plt.ylabel("# of Immigrants")
...plt.text(2000, 6000, '2010 Earthquake-->')
...plt.show()

I have no clue where these x-values are coming from, but they are not the index values that I intended to use. How do I generate this plot with the correct x-values?

I know that I can leave the index values a string and use the position of the index for the added text in the plot (i.e. skip the convert to string and use plt.text(20, 6000, '2010 Earthquake-->') ), but I'd much rather use the actual year. Can you please tell me how to do this correctly and what I'm doing wrong?

---

Here's the full code for anyone that wanted it. I am still curious why it automatically offset the tick marks. This is for an EdX course on data visualization and the .xlsx data is safe. The dataframe that I was calling df above is actually named "haiti" in this code.

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

#import data
df_can = pd.read_excel(
        "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx",
        sheetname="Canada by Citizenship", skiprows=range(20), skip_footer=2)

#pre-process data a bit
df_can.columns.tolist()
df_can.index.tolist()
df_can.drop(["AREA", "REG", "DEV", "Type", "Coverage"], axis=1, inplace=True)
df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True) #rename some columns to be more intuitive
df_can["Total"] = df_can.sum(axis=1) #add total # of immigrants column
df_can.set_index("Country", inplace=True) #change index from number to Country
df_can.columns = list(map(str,df_can.columns)) #convert years to string to avoid confusion
years = list(map(str,range(1980,2014))) #useful for plotting later

#line plot with mpl
mpl.style.use('ggplot')
haiti = df_can.loc['Haiti',years]
haiti.index = haiti.index.map(int)

haiti.plot(kind="line",figsize=(14,8))
plt.title('Immigration Trend of Top 5 Countries')
plt.ylabel('Number of Immigrants')
plt.xlabel('Years')
plt.text(2005, 6000, '2010 Earthquake---------->')
plt.show()

Sheldore :

You can turn off the offset as follows: You failed to provide us a working code so only you can test if it works. This has been explained in detail here

Way 1

fig, ax = plt.subplots()

df.plot(ax=ax)
ax.ticklabel_format(useOffset=False)

Way 2

 ax = df.plot()
 ax.ticklabel_format(useOffset=False)